{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 策略逻辑：\n",
    "1. 60分钟看长短期MA趋势， 15分钟做均线择时。\n",
    "2. 加1：2的止损止盈比例\n",
    "---\n",
    "## 增加代码\n",
    "1. onBar编辑止损监控的洗价器\n",
    "2. self.cancelAll(): 撤掉为下的单子，避免重复下单"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "这里的Demo是一个最简单的双均线策略实现\n",
    "\"\"\"\n",
    "\n",
    "from __future__ import division\n",
    "\n",
    "from vnpy.trader.vtConstant import EMPTY_STRING, EMPTY_FLOAT\n",
    "from vnpy.trader.app.ctaStrategy.ctaTemplate import (CtaTemplate, \n",
    "                                                     BarGenerator,\n",
    "                                                     ArrayManager)\n",
    "import talib as ta\n",
    "\n",
    "########################################################################\n",
    "# 策略继承CtaTemplate\n",
    "class MultiFrameMaStrategy(CtaTemplate):\n",
    "    \"\"\"双指数均线策略Demo\"\"\"\n",
    "    className = 'MultiFrameMaStrategy'\n",
    "    author = u'用Python的交易员'\n",
    "    \n",
    "    # 策略交易标的的列表\n",
    "    symbolList = []         # 初始化为空\n",
    "    posDict = {}  # 初始化仓位字典\n",
    "    \n",
    "    # 多空仓位\n",
    "    Longpos = EMPTY_STRING        # 多头品种仓位\n",
    "    Shortpos = EMPTY_STRING       # 空头品种仓位\n",
    "    \n",
    "    # 策略参数\n",
    "    fastWindow = 40     # 快速均线参数\n",
    "    slowWindow = 70     # 慢速均线参数\n",
    "    initDays = 1       # 初始化数据所用的天数\n",
    "    stopRatio = 0.085   # 止损比例\n",
    "    \n",
    "    # 策略变量\n",
    "    fastMa0 = EMPTY_FLOAT   # 当前最新的快速EMA\n",
    "    fastMa1 = EMPTY_FLOAT   # 上一根的快速EMA\n",
    "    slowMa0 = EMPTY_FLOAT   # 当前最新的慢速EMA\n",
    "    slowMa1 = EMPTY_FLOAT   # 上一根的慢速EMA\n",
    "    transactionPrice = EMPTY_FLOAT # 记录成交价格\n",
    "    maTrend = 0             # 均线趋势，多头1，空头-1\n",
    "    \n",
    "    # 参数列表，保存了参数的名称\n",
    "    paramList = ['name',\n",
    "                 'className',\n",
    "                 'author',\n",
    "                 'vtSymbol',\n",
    "                 'symbolList',\n",
    "                 'fastWindow',\n",
    "                 'slowWindow',\n",
    "                 'stopRatio']  \n",
    "    \n",
    "    # 变量列表，保存了变量的名称\n",
    "    varList = ['inited',\n",
    "               'trading',\n",
    "               'posDict',\n",
    "               'fastMa0',\n",
    "               'fastMa1',\n",
    "               'slowMa0',\n",
    "               'slowMa1',\n",
    "               'maTrend',\n",
    "               'transactionPrice']  \n",
    "    \n",
    "    # 同步列表，保存了需要保存到数据库的变量名称\n",
    "    syncList = ['posDict']\n",
    "\n",
    "    #----------------------------------------------------------------------\n",
    "    def __init__(self, ctaEngine, setting):\n",
    "        \n",
    "        # 首先找到策略的父类（就是类CtaTemplate），然后把DoubleMaStrategy的对象转换为类CtaTemplate的对象\n",
    "        super(MultiFrameMaStrategy, self).__init__(ctaEngine, setting)\n",
    "        \n",
    "        # 生成仓位记录的字典\n",
    "        symbol = self.symbolList[0]\n",
    "        self.Longpos = symbol.replace('.','_')+\"_LONG\"\n",
    "        self.Shortpos = symbol.replace('.','_')+\"_SHORT\"\n",
    "        \n",
    "        \n",
    "        self.bg60 = BarGenerator(self.onBar, 60, self.on60MinBar)\n",
    "        self.bg60Dict = {\n",
    "            sym: self.bg60\n",
    "            for sym in self.symbolList\n",
    "        }\n",
    "        \n",
    "        \n",
    "        self.bg15 = BarGenerator(self.onBar, 15, self.on15MinBar)\n",
    "        self.bg15Dict = {\n",
    "            sym: self.bg15\n",
    "            for sym in self.symbolList\n",
    "        }\n",
    "        \n",
    "        # 生成Bar数组\n",
    "        self.am60Dict = {\n",
    "            sym: ArrayManager(size=self.slowWindow+10)\n",
    "            for sym in self.symbolList\n",
    "        }\n",
    "      \n",
    "        self.am15Dict = {\n",
    "            sym: ArrayManager(size=self.slowWindow+10)\n",
    "            for sym in self.symbolList\n",
    "        }\n",
    "    \n",
    "    #----------------------------------------------------------------------\n",
    "    def onInit(self):\n",
    "        \"\"\"初始化策略（必须由用户继承实现）\"\"\"\n",
    "        self.writeCtaLog(u'双EMA演示策略初始化')\n",
    "        # 初始化仓位字典\n",
    "        self.ctaEngine.initPosition(self)\n",
    "        # 初始化历史数据天数\n",
    "        initData = self.loadBar(self.initDays)\n",
    "        for bar in initData:\n",
    "            self.onBar(bar)\n",
    "        \n",
    "        self.putEvent()\n",
    "\n",
    "    #----------------------------------------------------------------------\n",
    "    def onStart(self):\n",
    "        \"\"\"启动策略（必须由用户继承实现）\"\"\"\n",
    "        self.writeCtaLog(u'双EMA演示策略启动')\n",
    "        self.putEvent()\n",
    "    \n",
    "    #----------------------------------------------------------------------\n",
    "    def onStop(self):\n",
    "        \"\"\"停止策略（必须由用户继承实现）\"\"\"\n",
    "        self.writeCtaLog(u'双EMA演示策略停止')\n",
    "        self.putEvent()\n",
    "        \n",
    "    #----------------------------------------------------------------------\n",
    "    def onTick(self, tick):\n",
    "        \"\"\"收到行情TICK推送（必须由用户继承实现）\"\"\"\n",
    "        pass\n",
    "    \n",
    "    #----------------------------------------------------------------------\n",
    "    def onBar(self, bar):\n",
    "        \"\"\"收到Bar推送（必须由用户继承实现）\"\"\"\n",
    "\n",
    "        symbol = bar.vtSymbol\n",
    "        # 基于60分钟判断趋势过滤，因此先更新\n",
    "        bg60 = self.bg60Dict[symbol]\n",
    "        bg60.updateBar(bar)\n",
    "        \n",
    "        # 基于15分钟判断\n",
    "        bg15 = self.bg15Dict[symbol]\n",
    "        bg15.updateBar(bar)\n",
    "        \n",
    "        # 洗价器\n",
    "        if (self.posDict[self.Longpos] > 0):\n",
    "            if (bar.close<self.transactionPrice*(1-self.stopRatio)) or (bar.close>self.transactionPrice*(1+2*self.stopRatio)):\n",
    "                self.cancelAll()\n",
    "                self.sell(symbol,bar.close*0.9, 1)\n",
    "        elif (self.posDict[self.Shortpos] > 0):\n",
    "            if (bar.close>self.transactionPrice*(1+self.stopRatio)) or  (bar.close<self.transactionPrice*(1-2*self.stopRatio)):\n",
    "                self.cancelAll()\n",
    "                self.cover(symbol,bar.close*1.1, 1)\n",
    "        \n",
    "    #----------------------------------------------------------------------\n",
    "    def on60MinBar(self, bar):\n",
    "        \"\"\"60分钟K线推送\"\"\"\n",
    "        symbol = bar.vtSymbol\n",
    "        am60 = self.am60Dict[symbol]\n",
    "        am60.updateBar(bar)\n",
    "        \n",
    "        if not am60.inited:\n",
    "            return\n",
    "        \n",
    "        # 计算均线并判断趋势\n",
    "        fastMa = ta.MA(am60.close, self.fastWindow)\n",
    "        slowMa = ta.MA(am60.close, self.slowWindow)\n",
    "#         print(fastMa)\n",
    "        \n",
    "        if fastMa[-1] > slowMa[-1]:\n",
    "            self.maTrend = 1\n",
    "        else:\n",
    "            self.maTrend = -1\n",
    "    \n",
    "    #----------------------------------------------------------------------\n",
    "    def on15MinBar(self, bar):\n",
    "        \"\"\"收到Bar推送（必须由用户继承实现）\"\"\"\n",
    "        self.cancelAll() # 全部撤单\n",
    "        symbol = bar.vtSymbol\n",
    "        \n",
    "        \n",
    "        am15 = self.am15Dict[symbol]\n",
    "        am15.updateBar(bar)\n",
    "        if not am15.inited:\n",
    "            return\n",
    "\n",
    "        fastMa = ta.EMA(am15.close, self.fastWindow)\n",
    "       \n",
    "        self.fastMa0 = fastMa[-1]\n",
    "        self.fastMa1 = fastMa[-2]\n",
    "        \n",
    "        slowMa = ta.EMA(am15.close, self.slowWindow)\n",
    "        self.slowMa0 = slowMa[-1]\n",
    "        self.slowMa1 = slowMa[-2]\n",
    "\n",
    "        # 判断买卖\n",
    "        crossOver = self.fastMa0>self.fastMa1 and self.slowMa0>self.slowMa1     # 均线上涨\n",
    "        crossBelow = self.fastMa0<self.fastMa1 and self.slowMa0<self.slowMa1    # 均线下跌\n",
    "        \n",
    "\n",
    "        \n",
    "        # 金叉和死叉的条件是互斥\n",
    "        if crossOver and self.maTrend==1:\n",
    "            # 如果金叉时手头没有持仓，则直接做多\n",
    "            if (self.posDict[self.Longpos]==0) and (self.posDict[self.Shortpos]==0):\n",
    "                self.buy(symbol,bar.close*1.1, 1)\n",
    "            # 如果有空头持仓，则先平空，再做多\n",
    "            elif self.posDict[self.Shortpos] == 1:\n",
    "                self.cancelAll()\n",
    "                self.cover(symbol,bar.close*1.1, 1)\n",
    "                self.buy(symbol,bar.close*1.1, 1)\n",
    "\n",
    "        # 死叉和金叉相反\n",
    "        elif crossBelow and self.maTrend==-1:\n",
    "            if (self.posDict[self.Longpos]==0) and (self.posDict[self.Shortpos]==0):\n",
    "                self.short(symbol,bar.close*0.9, 1)\n",
    "            elif self.posDict[self.Longpos] == 1:\n",
    "                self.cancelAll()\n",
    "                self.sell(symbol,bar.close*0.9, 1)\n",
    "                self.short(symbol,bar.close*0.9, 1)\n",
    "\n",
    "#         print(self.posDict)\n",
    "        # 发出状态更新事件\n",
    "        self.putEvent()\n",
    "    \n",
    "    #----------------------------------------------------------------------\n",
    "    def onOrder(self, order):\n",
    "        \"\"\"收到委托变化推送（必须由用户继承实现）\"\"\"\n",
    "        # 对于无需做细粒度委托控制的策略，可以忽略onOrder\n",
    "        pass\n",
    "    \n",
    "    #----------------------------------------------------------------------\n",
    "    def onTrade(self, trade):\n",
    "        \"\"\"收到成交推送（必须由用户继承实现）\"\"\"\n",
    "        # 对于无需做细粒度委托控制的策略，可以忽略onOrder\n",
    "        self.transactionPrice = trade.price\n",
    "#         pass\n",
    "    \n",
    "    #----------------------------------------------------------------------\n",
    "    def onStopOrder(self, so):\n",
    "        \"\"\"停止单推送\"\"\"\n",
    "        pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 配置引擎参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-07-14 14:15:23.217742\t开始回测\n",
      "2018-07-14 14:15:23.217742\t策略初始化\n",
      "2018-07-14 14:15:23.217742\t载入历史数据。数据范围:[20171231,20180101)\n",
      "2018-07-14 14:15:23.384572\t载入完成，数据量：1414\n",
      "2018-07-14 14:15:23.394562\t策略初始化完成\n",
      "2018-07-14 14:15:23.394562\t策略启动完成\n",
      "2018-07-14 14:15:23.394562\t开始回放回测数据,回测范围:[20180101,20180701)\n",
      "2018-07-14 14:15:23.395560\t载入历史数据。数据范围:[20180101,20180311)\n",
      "2018-07-14 14:15:31.467320\t载入完成，数据量：99357\n",
      "2018-07-14 14:15:31.467320\t当前回放数据:[20180101,20180311)\n",
      "2018-07-14 14:15:32.632130\t载入历史数据。数据范围:[20180311,20180519)\n",
      "2018-07-14 14:15:40.967621\t载入完成，数据量：99073\n",
      "2018-07-14 14:15:41.003583\t当前回放数据:[20180311,20180519)\n",
      "2018-07-14 14:15:42.184378\t载入历史数据。数据范围:[20180519,20180701)\n",
      "2018-07-14 14:15:47.154303\t载入完成，数据量：61359\n",
      "2018-07-14 14:15:47.192265\t当前回放数据:[20180519,20180701)\n",
      "2018-07-14 14:15:47.981460\t数据回放结束ss: 100%    \n"
     ]
    }
   ],
   "source": [
    "if __name__==\"__main__\":\n",
    "    from vnpy.trader.app.ctaStrategy.ctaBacktesting import BacktestingEngine, OptimizationSetting, MINUTE_DB_NAME\n",
    "    # 创建回测引擎对象\n",
    "    engine = BacktestingEngine()\n",
    "    # 设置回测使用的数据\n",
    "    engine.setBacktestingMode(engine.BAR_MODE)    # 设置引擎的回测模式为K线\n",
    "    engine.setDatabase(MINUTE_DB_NAME)  # 设置使用的历史数据库\n",
    "    engine.setStartDate('20180101',initDays=1)               # 设置回测用的数据起始日期\n",
    "    engine.setEndDate('20180630')\n",
    "    # 配置回测引擎参数\n",
    "    engine.setSlippage(0.2)     # 设置滑点为股指1跳\n",
    "    engine.setRate(1/1000)   # 设置手续费万0.3\n",
    "    engine.setSize(1)         # 设置股指合约大小\n",
    "    # engine.setPriceTick(0.0001)    # 设置股指最小价格变动\n",
    "    engine.setCapital(1000000)  # 设置回测本金\n",
    "\n",
    "    # # 在引擎中创建策略对象\n",
    "    d = {'symbolList':['tBTCUSD:bitfinex']}          # 策略参数配置\n",
    "    engine.initStrategy(MultiFrameMaStrategy, d)    # 创建策略对象\n",
    "    engine.runBacktesting()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TradeID: 1, Time: 2018-01-04 07:31:00, Direction: 多, Price: 15019.0, Volume: 1\n",
      "TradeID: 2, Time: 2018-01-08 16:01:00, Direction: 空, Price: 15886.0, Volume: 1\n",
      "TradeID: 3, Time: 2018-01-08 16:01:00, Direction: 空, Price: 15886.0, Volume: 1\n",
      "TradeID: 4, Time: 2018-01-11 11:53:00, Direction: 多, Price: 13160.0, Volume: 1\n",
      "TradeID: 5, Time: 2018-01-11 12:01:00, Direction: 空, Price: 13261.0, Volume: 1\n",
      "TradeID: 6, Time: 2018-01-13 20:38:00, Direction: 多, Price: 14419.0, Volume: 1\n",
      "TradeID: 7, Time: 2018-01-13 23:01:00, Direction: 多, Price: 14374.0, Volume: 1\n",
      "TradeID: 8, Time: 2018-01-14 20:40:00, Direction: 空, Price: 13152.0, Volume: 1\n",
      "TradeID: 9, Time: 2018-01-15 07:01:00, Direction: 多, Price: 13546.0, Volume: 1\n",
      "TradeID: 10, Time: 2018-01-15 10:31:00, Direction: 空, Price: 13424.0, Volume: 1\n",
      "TradeID: 11, Time: 2018-01-15 10:31:00, Direction: 空, Price: 13424.0, Volume: 1\n",
      "TradeID: 12, Time: 2018-01-16 17:50:00, Direction: 多, Price: 11125.0, Volume: 1\n",
      "TradeID: 13, Time: 2018-01-16 18:01:00, Direction: 空, Price: 11411.0, Volume: 1\n",
      "TradeID: 14, Time: 2018-01-17 22:37:00, Direction: 多, Price: 9435.2, Volume: 1\n",
      "TradeID: 15, Time: 2018-01-17 22:46:00, Direction: 空, Price: 9654.0, Volume: 1\n",
      "TradeID: 16, Time: 2018-01-18 03:48:00, Direction: 多, Price: 10517.0, Volume: 1\n",
      "TradeID: 17, Time: 2018-01-18 15:46:00, Direction: 空, Price: 10584.0, Volume: 1\n",
      "TradeID: 18, Time: 2018-01-18 19:08:00, Direction: 多, Price: 11481.0, Volume: 1\n",
      "TradeID: 19, Time: 2018-01-19 02:01:00, Direction: 空, Price: 11134.0, Volume: 1\n",
      "TradeID: 20, Time: 2018-01-19 14:31:00, Direction: 多, Price: 11385.0, Volume: 1\n",
      "TradeID: 21, Time: 2018-01-19 14:31:00, Direction: 多, Price: 11385.0, Volume: 1\n",
      "TradeID: 22, Time: 2018-01-22 19:46:00, Direction: 空, Price: 11368.0, Volume: 1\n",
      "TradeID: 23, Time: 2018-01-22 19:46:00, Direction: 空, Price: 11368.0, Volume: 1\n",
      "TradeID: 24, Time: 2018-01-25 09:01:00, Direction: 多, Price: 11268.0, Volume: 1\n",
      "TradeID: 25, Time: 2018-01-25 09:01:00, Direction: 多, Price: 11268.0, Volume: 1\n",
      "TradeID: 26, Time: 2018-01-26 17:52:00, Direction: 空, Price: 10301.0, Volume: 1\n",
      "TradeID: 27, Time: 2018-01-27 00:01:00, Direction: 多, Price: 11096.0, Volume: 1\n",
      "TradeID: 28, Time: 2018-01-27 07:01:00, Direction: 空, Price: 10779.0, Volume: 1\n",
      "TradeID: 29, Time: 2018-01-27 07:01:00, Direction: 空, Price: 10779.0, Volume: 1\n",
      "TradeID: 30, Time: 2018-01-28 12:01:00, Direction: 多, Price: 11540.0, Volume: 1\n",
      "TradeID: 31, Time: 2018-01-28 12:01:00, Direction: 多, Price: 11540.0, Volume: 1\n",
      "TradeID: 32, Time: 2018-01-30 12:01:00, Direction: 空, Price: 10966.0, Volume: 1\n",
      "TradeID: 33, Time: 2018-01-30 12:01:00, Direction: 空, Price: 10966.0, Volume: 1\n",
      "TradeID: 34, Time: 2018-02-02 00:37:00, Direction: 多, Price: 9101.0, Volume: 1\n",
      "TradeID: 35, Time: 2018-02-02 00:46:00, Direction: 空, Price: 9227.0, Volume: 1\n",
      "TradeID: 36, Time: 2018-02-05 19:11:00, Direction: 多, Price: 7655.0, Volume: 1\n",
      "TradeID: 37, Time: 2018-02-05 19:16:00, Direction: 空, Price: 7666.9, Volume: 1\n",
      "TradeID: 38, Time: 2018-02-06 11:02:00, Direction: 多, Price: 6360.1, Volume: 1\n",
      "TradeID: 39, Time: 2018-02-06 11:16:00, Direction: 空, Price: 6295.6, Volume: 1\n",
      "TradeID: 40, Time: 2018-02-06 17:36:00, Direction: 多, Price: 6871.6, Volume: 1\n",
      "TradeID: 41, Time: 2018-02-06 19:16:00, Direction: 空, Price: 6307.9, Volume: 1\n",
      "TradeID: 42, Time: 2018-02-06 21:09:00, Direction: 多, Price: 6854.5, Volume: 1\n",
      "TradeID: 43, Time: 2018-02-08 05:01:00, Direction: 多, Price: 8159.8, Volume: 1\n",
      "TradeID: 44, Time: 2018-02-12 07:31:00, Direction: 空, Price: 8141.8, Volume: 1\n",
      "TradeID: 45, Time: 2018-02-12 07:31:00, Direction: 空, Price: 8141.8, Volume: 1\n",
      "TradeID: 46, Time: 2018-02-12 18:35:00, Direction: 多, Price: 8853.0, Volume: 1\n",
      "TradeID: 47, Time: 2018-02-13 08:01:00, Direction: 多, Price: 8902.0, Volume: 1\n",
      "TradeID: 48, Time: 2018-02-17 10:07:00, Direction: 空, Price: 10418.0, Volume: 1\n",
      "TradeID: 49, Time: 2018-02-17 10:16:00, Direction: 多, Price: 10397.0, Volume: 1\n",
      "TradeID: 50, Time: 2018-02-22 09:01:00, Direction: 空, Price: 10477.0, Volume: 1\n",
      "TradeID: 51, Time: 2018-02-22 09:01:00, Direction: 空, Price: 10477.0, Volume: 1\n",
      "TradeID: 52, Time: 2018-02-27 10:46:00, Direction: 多, Price: 10217.0, Volume: 1\n",
      "TradeID: 53, Time: 2018-02-27 10:46:00, Direction: 多, Price: 10217.0, Volume: 1\n",
      "TradeID: 54, Time: 2018-03-07 04:01:00, Direction: 空, Price: 10710.0, Volume: 1\n",
      "TradeID: 55, Time: 2018-03-07 04:01:00, Direction: 空, Price: 10710.0, Volume: 1\n",
      "TradeID: 56, Time: 2018-03-09 10:13:00, Direction: 多, Price: 8886.0, Volume: 1\n",
      "TradeID: 57, Time: 2018-03-09 10:16:00, Direction: 空, Price: 8954.0, Volume: 1\n",
      "TradeID: 58, Time: 2018-03-11 20:01:00, Direction: 多, Price: 9028.6, Volume: 1\n",
      "TradeID: 59, Time: 2018-03-11 20:01:00, Direction: 多, Price: 9028.6, Volume: 1\n",
      "TradeID: 60, Time: 2018-03-14 12:16:00, Direction: 空, Price: 9113.0, Volume: 1\n",
      "TradeID: 61, Time: 2018-03-14 12:16:00, Direction: 空, Price: 9113.0, Volume: 1\n",
      "TradeID: 62, Time: 2018-03-18 10:32:00, Direction: 多, Price: 7563.6, Volume: 1\n",
      "TradeID: 63, Time: 2018-03-18 12:01:00, Direction: 空, Price: 7634.9, Volume: 1\n",
      "TradeID: 64, Time: 2018-03-19 05:58:00, Direction: 多, Price: 8284.5, Volume: 1\n",
      "TradeID: 65, Time: 2018-03-20 01:31:00, Direction: 空, Price: 8306.1, Volume: 1\n",
      "TradeID: 66, Time: 2018-03-20 05:01:00, Direction: 多, Price: 8413.0, Volume: 1\n",
      "TradeID: 67, Time: 2018-03-20 05:01:00, Direction: 多, Price: 8413.0, Volume: 1\n",
      "TradeID: 68, Time: 2018-03-23 14:01:00, Direction: 空, Price: 8420.0, Volume: 1\n",
      "TradeID: 69, Time: 2018-03-23 14:01:00, Direction: 空, Price: 8420.0, Volume: 1\n",
      "TradeID: 70, Time: 2018-03-25 22:46:00, Direction: 多, Price: 8586.6, Volume: 1\n",
      "TradeID: 71, Time: 2018-03-25 22:46:00, Direction: 多, Price: 8586.6, Volume: 1\n",
      "TradeID: 72, Time: 2018-03-26 09:01:00, Direction: 空, Price: 8371.0, Volume: 1\n",
      "TradeID: 73, Time: 2018-03-26 09:01:00, Direction: 空, Price: 8371.0, Volume: 1\n",
      "TradeID: 74, Time: 2018-03-30 05:53:00, Direction: 多, Price: 6946.0, Volume: 1\n",
      "TradeID: 75, Time: 2018-03-30 06:01:00, Direction: 空, Price: 7102.9, Volume: 1\n",
      "TradeID: 76, Time: 2018-04-03 13:01:00, Direction: 多, Price: 7354.7, Volume: 1\n",
      "TradeID: 77, Time: 2018-04-03 13:01:00, Direction: 多, Price: 7354.7, Volume: 1\n",
      "TradeID: 78, Time: 2018-04-05 06:40:00, Direction: 空, Price: 6700.1, Volume: 1\n",
      "TradeID: 79, Time: 2018-04-05 15:01:00, Direction: 空, Price: 6740.2, Volume: 1\n",
      "TradeID: 80, Time: 2018-04-08 06:01:00, Direction: 多, Price: 6974.9, Volume: 1\n",
      "TradeID: 81, Time: 2018-04-08 06:01:00, Direction: 多, Price: 6974.9, Volume: 1\n",
      "TradeID: 82, Time: 2018-04-10 13:16:00, Direction: 空, Price: 6737.2, Volume: 1\n",
      "TradeID: 83, Time: 2018-04-10 13:16:00, Direction: 空, Price: 6737.2, Volume: 1\n",
      "TradeID: 84, Time: 2018-04-12 10:01:00, Direction: 多, Price: 6933.9, Volume: 1\n",
      "TradeID: 85, Time: 2018-04-12 10:01:00, Direction: 多, Price: 6933.9, Volume: 1\n",
      "TradeID: 86, Time: 2018-04-13 15:55:00, Direction: 空, Price: 8115.2, Volume: 1\n",
      "TradeID: 87, Time: 2018-04-13 16:01:00, Direction: 多, Price: 8155.0, Volume: 1\n",
      "TradeID: 88, Time: 2018-04-17 21:16:00, Direction: 空, Price: 8054.7, Volume: 1\n",
      "TradeID: 89, Time: 2018-04-17 21:16:00, Direction: 空, Price: 8054.7, Volume: 1\n",
      "TradeID: 90, Time: 2018-04-19 07:01:00, Direction: 多, Price: 8163.2, Volume: 1\n",
      "TradeID: 91, Time: 2018-04-19 07:01:00, Direction: 多, Price: 8163.2, Volume: 1\n",
      "TradeID: 92, Time: 2018-04-25 06:30:00, Direction: 空, Price: 9573.8, Volume: 1\n",
      "TradeID: 93, Time: 2018-04-25 06:31:00, Direction: 多, Price: 9582.7, Volume: 1\n",
      "TradeID: 94, Time: 2018-04-26 08:06:00, Direction: 空, Price: 8762.2, Volume: 1\n",
      "TradeID: 95, Time: 2018-04-26 21:31:00, Direction: 空, Price: 8845.9, Volume: 1\n",
      "TradeID: 96, Time: 2018-04-28 13:16:00, Direction: 多, Price: 9170.7, Volume: 1\n",
      "TradeID: 97, Time: 2018-04-28 13:16:00, Direction: 多, Price: 9170.7, Volume: 1\n",
      "TradeID: 98, Time: 2018-05-01 07:01:00, Direction: 空, Price: 9196.1, Volume: 1\n",
      "TradeID: 99, Time: 2018-05-01 07:01:00, Direction: 空, Price: 9196.1, Volume: 1\n",
      "TradeID: 100, Time: 2018-05-03 16:01:00, Direction: 多, Price: 9233.6, Volume: 1\n",
      "TradeID: 101, Time: 2018-05-03 16:01:00, Direction: 多, Price: 9233.6, Volume: 1\n",
      "TradeID: 102, Time: 2018-05-07 10:01:00, Direction: 空, Price: 9354.1, Volume: 1\n",
      "TradeID: 103, Time: 2018-05-07 10:01:00, Direction: 空, Price: 9354.1, Volume: 1\n",
      "TradeID: 104, Time: 2018-05-14 22:01:00, Direction: 多, Price: 8699.9, Volume: 1\n",
      "TradeID: 105, Time: 2018-05-14 22:01:00, Direction: 多, Price: 8699.9, Volume: 1\n",
      "TradeID: 106, Time: 2018-05-16 17:01:00, Direction: 空, Price: 8210.0, Volume: 1\n",
      "TradeID: 107, Time: 2018-05-16 17:01:00, Direction: 空, Price: 8210.0, Volume: 1\n",
      "TradeID: 108, Time: 2018-05-20 11:01:00, Direction: 多, Price: 8267.0, Volume: 1\n",
      "TradeID: 109, Time: 2018-05-20 11:01:00, Direction: 多, Price: 8267.0, Volume: 1\n",
      "TradeID: 110, Time: 2018-05-23 03:01:00, Direction: 空, Price: 8217.4, Volume: 1\n",
      "TradeID: 111, Time: 2018-05-23 03:01:00, Direction: 空, Price: 8217.4, Volume: 1\n",
      "TradeID: 112, Time: 2018-05-30 13:01:00, Direction: 多, Price: 7485.5, Volume: 1\n",
      "TradeID: 113, Time: 2018-05-30 13:01:00, Direction: 多, Price: 7485.5, Volume: 1\n",
      "TradeID: 114, Time: 2018-06-05 09:01:00, Direction: 空, Price: 7493.9, Volume: 1\n",
      "TradeID: 115, Time: 2018-06-05 09:01:00, Direction: 空, Price: 7493.9, Volume: 1\n",
      "TradeID: 116, Time: 2018-06-07 07:01:00, Direction: 多, Price: 7660.4, Volume: 1\n",
      "TradeID: 117, Time: 2018-06-07 07:01:00, Direction: 多, Price: 7660.4, Volume: 1\n",
      "TradeID: 118, Time: 2018-06-09 17:01:00, Direction: 空, Price: 7630.0, Volume: 1\n",
      "TradeID: 119, Time: 2018-06-09 17:01:00, Direction: 空, Price: 7630.0, Volume: 1\n",
      "TradeID: 120, Time: 2018-06-14 00:24:00, Direction: 多, Price: 6284.1, Volume: 1\n",
      "TradeID: 121, Time: 2018-06-14 00:31:00, Direction: 空, Price: 6130.5, Volume: 1\n",
      "TradeID: 122, Time: 2018-06-15 02:06:00, Direction: 多, Price: 6675.5, Volume: 1\n",
      "TradeID: 123, Time: 2018-06-15 13:16:00, Direction: 空, Price: 6551.58568068, Volume: 1\n",
      "TradeID: 124, Time: 2018-06-16 00:31:00, Direction: 多, Price: 6572.4, Volume: 1\n",
      "TradeID: 125, Time: 2018-06-16 00:31:00, Direction: 多, Price: 6572.4, Volume: 1\n",
      "TradeID: 126, Time: 2018-06-17 19:46:00, Direction: 空, Price: 6499.9, Volume: 1\n",
      "TradeID: 127, Time: 2018-06-17 19:46:00, Direction: 空, Price: 6499.9, Volume: 1\n",
      "TradeID: 128, Time: 2018-06-18 16:46:00, Direction: 多, Price: 6481.2, Volume: 1\n",
      "TradeID: 129, Time: 2018-06-18 16:46:00, Direction: 多, Price: 6481.2, Volume: 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TradeID: 130, Time: 2018-06-21 21:01:00, Direction: 空, Price: 6716.1, Volume: 1\n",
      "TradeID: 131, Time: 2018-06-21 21:01:00, Direction: 空, Price: 6716.1, Volume: 1\n",
      "TradeID: 132, Time: 2018-06-26 06:01:00, Direction: 多, Price: 6268.8, Volume: 1\n",
      "TradeID: 133, Time: 2018-06-26 06:01:00, Direction: 多, Price: 6268.8, Volume: 1\n",
      "TradeID: 134, Time: 2018-06-27 22:01:00, Direction: 空, Price: 6094.0, Volume: 1\n",
      "TradeID: 135, Time: 2018-06-27 22:01:00, Direction: 空, Price: 6094.0, Volume: 1\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'136'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-b53c4145b5bf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# 显示前10条成交记录\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0md\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtradeDict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__dict__\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'TradeID: %s, Time: %s, Direction: %s, Price: %s, Volume: %s'\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'tradeID'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'dt'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'direction'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'price'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'volume'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: '136'"
     ]
    }
   ],
   "source": [
    "# 显示前10条成交记录\n",
    "for i in range(1000):\n",
    "    d = engine.tradeDict[str(i+1)].__dict__\n",
    "    print('TradeID: %s, Time: %s, Direction: %s, Price: %s, Volume: %s' %(d['tradeID'], d['dt'], d['direction'], d['price'], d['volume']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-07-14 14:22:50.850719\t计算按日统计结果\n",
      "2018-07-14 14:22:50.876692\t------------------------------\n",
      "2018-07-14 14:22:50.876692\t首个交易日：\t2018-01-01\n",
      "2018-07-14 14:22:50.876692\t最后交易日：\t2018-06-30\n",
      "2018-07-14 14:22:50.876692\t总交易日：\t181\n",
      "2018-07-14 14:22:50.876692\t盈利交易日\t94\n",
      "2018-07-14 14:22:50.876692\t亏损交易日：\t84\n",
      "2018-07-14 14:22:50.876692\t起始资金：\t1000000\n",
      "2018-07-14 14:22:50.876692\t结束资金：\t1,009,306.07\n",
      "2018-07-14 14:22:50.876692\t总收益率：\t0.93%\n",
      "2018-07-14 14:22:50.876692\t年化收益：\t1.23%\n",
      "2018-07-14 14:22:50.876692\t总盈亏：\t9,306.07\n",
      "2018-07-14 14:22:50.876692\t最大回撤: \t-4,434.99\n",
      "2018-07-14 14:22:50.877691\t百分比最大回撤: -0.44%\n",
      "2018-07-14 14:22:50.877691\t总手续费：\t1,282.02\n",
      "2018-07-14 14:22:50.877691\t总滑点：\t27.8\n",
      "2018-07-14 14:22:50.877691\t总成交金额：\t1,282,016.59\n",
      "2018-07-14 14:22:50.877691\t总成交笔数：\t139\n",
      "2018-07-14 14:22:50.877691\t日均盈亏：\t51.41\n",
      "2018-07-14 14:22:50.877691\t日均手续费：\t7.08\n",
      "2018-07-14 14:22:50.877691\t日均滑点：\t0.15\n",
      "2018-07-14 14:22:50.877691\t日均成交金额：\t7,082.96\n",
      "2018-07-14 14:22:50.877691\t日均成交笔数：\t0.77\n",
      "2018-07-14 14:22:50.877691\t日均收益率：\t0.01%\n",
      "2018-07-14 14:22:50.877691\t收益标准差：\t0.05%\n",
      "2018-07-14 14:22:50.877691\tSharpe Ratio：\t1.51\n"
     ]
    },
    {
     "data": {
      "image/png": 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b2HPsdJun+9rqZFYZzg7WdPdwbNN93T0cCfbvSnJ22U11RN0PsRn8dCD7mu9f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\n1apXXnlFRUVFev/999W5c2fdddddysrK0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      "text/plain": [
       "<matplotlib.figure.Figure at 0x260454b0828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-07-14 14:22:52.557976\t计算回测结果\n",
      "2018-07-14 14:22:52.562971\t------------------------------\n",
      "2018-07-14 14:22:52.563970\t第一笔交易：\t2018-01-07 06:18:00\n",
      "2018-07-14 14:22:52.563970\t最后一笔交易：\t2018-06-30 23:59:00\n",
      "2018-07-14 14:22:52.563970\t总交易次数：\t70\n",
      "2018-07-14 14:22:52.563970\t总盈亏：\t9,299.49\n",
      "2018-07-14 14:22:52.563970\t最大回撤: \t-4,749.01\n",
      "2018-07-14 14:22:52.563970\t平均每笔盈利：\t132.85\n",
      "2018-07-14 14:22:52.563970\t平均每笔滑点：\t0.4\n",
      "2018-07-14 14:22:52.563970\t平均每笔佣金：\t18.41\n",
      "2018-07-14 14:22:52.563970\t胜率\t\t37.14%\n",
      "2018-07-14 14:22:52.563970\t盈利交易平均值\t1,048.56\n",
      "2018-07-14 14:22:52.563970\t亏损交易平均值\t-408.25\n",
      "2018-07-14 14:22:52.563970\t盈亏比：\t2.57\n"
     ]
    },
    {
     "data": {
      "image/png": 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uRyEiIg5wvO3aQw89dNS2SZMmHbUtIyODjIyMMtvi4uIYP368bbGJiByybh2c\nfbbbUYiIiAO0MIeIyJF++w3i4yHK8TkDERFxgRJiEZEjff21+g+LiIQRJcQiIkfSghwiImFFCbGI\nyJG8XnWYEBEJI0qIRUSOtH49pKS4HYWIiDhECbGISGl79kBSEkRGuh2JiIg4RAmxiEhpXq/qh0VE\nwowSYhGR0lQ/LCISdipssrlkyZIKD77gggssDUZExHVLl8J117kdhYiIOKjChLiiVeE8Hg9vvPGG\n5QGJiLjq22/hzDPdjkJERBxUYUKcnZ3tVBwiIu7btQuOPx4iVE0mIhJO/FqXdOnSpUycOJG9e/fi\n8/koLi5m27ZtzJkzx+74RESco/phEZGw5Nc0yJAhQ7j00ks5ePAgN910Ew0bNuTSSy+1OzYREWct\nXaqEWEQkDPmVENeqVYvrr7+etLQ0kpKSGDlyZKU33ImIhBwlxCIiYcmvhDg2NpZff/2VRo0asWLF\nCjweD3v37rU7NhERZ23YAI0bux2FiIg4zK+E+LbbbmPAgAFccsklzJgxgyuvvJLzzjvP7thERJzz\n889Qty54PG5HIiIiDvPrprqLLrqILl264PF4mD59Ops3byYxMdHu2EREnKMb6kREwlaFM8Tbt29n\n27Zt3HTTTezYsYNt27bx66+/kpiYSJ8+fZyKUUTEfqofFhEJW5UuzJGTk8PPP//MTTfddPigqCg6\ndepkd2wiIs4oKoKZM+G229yOREREXFBhQjx69GgA/vGPf3DnnXc6EpCIiOMGDYLrr4cGDdyORERE\nXFBhQjx16lRuvPFGCgsLefHFF4/6/l//+lfbAhMRccTbb8PmzTB2rNuRiIiISypMiH0+n1NxiIg4\nb906eOopmDNH3SVERMJYhQlxz549ATMTvGvXLrxeL5GRkbRp04batWs7EqCIiC3y8qBXL/jXv0D/\nn4mIhDW/+hC///77XHPNNfz3v/9l+vTpXHXVVcybN8/u2ERE7OHzwZ13wr33QvPmbkcjIiIu86sP\n8YQJE5g+fTonn3wyAD/++CN33303F198sa3BSQ3h8+njaAkuL71kZoV79XI7EhERCQJ+zRAnJCRQ\nt27dQ49PP/10oqOjbQtKapiHHoIPP3Q7ChHjiy9gyhR47jm3IxERkSDh1wxxSkoKffr04frrrycy\nMpJZs2ZRr149ZsyYAcC1115ra5AS4hYtgm++ga5d3Y5ESnv9dWJOOAFSU92OxDm//AJ9+8J770Fs\nrNvRiIgM1lhEAAAgAElEQVRIkPArIfb5fNSrV48FCxYAEBcXR1xcHDk5OYASYqnAwYNQUADFxfDt\nt9C0qdsRCZgyljFjSD7xRLj66vAoaTl4EG66yXSVaNjQ7WhERCSI+JUQlyzQUdr+/fupVauW5QFJ\nDVOSBGdkwMsvq9drsFi/Hpo1o+jgQfjPf+CGG9yOyH7Dh0P79tCli9uRiIhIkPErIf7444956aWX\n2Lt3Lz6fj+LiYvbt28eXX35pd3wS6pYvh5Yt4Zpr4LHHYN8+iItzOyr56CPo0oWtjRtz0sCB0Lkz\nJCW5HZU9Cgpg5EhYuRL+KPMSEREpza+b6p555hkGDx5MkyZNePbZZ+nevTtdVQ8q/lixwiTEUVHQ\nowdMnep2RAImIe7cmYN16sCAATB0qNsR2SMnBzp0gOOPh3ffhQi//ssTEZEw49dvh6SkJC688EJa\ntGhBbm4u/fr1Y/ny5XbHJjXB8uXQooX5+x13wD/+4W48Ymbpd+6E5GTz+JZbYNUq8HrdjctKe/fC\nAw/A4MGmo8T990NkpNtRiYhIkPIrIa5VqxabNm2iSZMmfPXVVxQWFpKbm2t3bFITbN8Op5xi/n7a\naXD66bB0qbsxhbvPP4dOnQ4/9njgxRehXz9z41momzfPzAo3aQKffgpnnul2RCIiEuT8SogHDBjA\niBEjuOSSS/jyyy9JS0vj0ksvtTs2CXU7dsDJJ5ftYPCXv8Df/+5eTHKofriMc86BSy4J7Z9Nbi7c\nc4/pIvHuu6a9mkokRETED379tli7di27d+8mJiaG5557jpNPPpmGalsklSmpHy6tUydTRrFnjysh\nCbBgAXTsePT2IUPgn/+Ebducj6m6DhwwHTPefNM8pwsuMIvA6P8nERGpAr+6TEybNo23334bgPr1\n6zNjxgwyMjLo2bOnrcFJiFux4nD9cAmPB269FV5/3dzMJc7auNGUrZS3KEVcHIwaZX4uwXbz4759\nJvFdu/bw18aN5vXUuDE0awazZsGpp7odqYiIhCC/EuKioqIySzVr2Wbxy/LlcNVVR2+/5Rb405/g\nvvv0kbbTyiuXKK1LFzNLXNl+Tlq1Crp1M3XB55xjVta7+WaTCEf59V+YiIhIhfz6bXLppZdy6623\ncsUVVwDwySef8Oc//9nWwKQGWL8eUlKO3l6nDrRpA3PmgGrRnfXRR/C3v1W8z3PPwZVXwsUXB0fP\n6E8+gUcfhd693Y5ERERqKL+m5x588EF69erFpk2b+OGHH7jlllvo379/tS+6a9cuLr74YjZs2MCW\nLVvIzMwkKyuL4cOHU1xcDJgyje7du5ORkcHcuXMBszpev379yMrKok+fPuzevbvaMYjN9u6FmJhj\nz+D17QsTJjgbU7grKIAffjDdFypy2mkm+XzySWfiqsz8+SY5FxERsYnfnzd26dKFLhZ8hFpUVMSw\nYcMOLfs8evRo+vfvT9u2bRk2bBizZ8+mZcuWZGdn884771BQUEBWVhbt27dnypQppKSk0K9fP2bO\nnMmECRMYMmRIwDGJDVatgvPOO/b3W7WCX36BrVuhfn3n4gpnCxeWfzNdef7yF7Pv2rWmTMEtxcWm\nVrhxY/diEBGRGs/xAs4xY8bQs2dP6tWrB8Dq1atJS0sDID09ncWLF7Ny5UpatWpFTEwMiYmJJCcn\ns27dOrxeLx3/+IWenp7OF1984XT44q/yOkwc6c474dVXnYlHqlYXHBkJL7xgEuM/PrVxxerVcO65\nZVv3iYiIWMzRO1KmT5/OCSecQMeOHfnHHyuW+Xw+PH/8souPjyc3N5e8vDwSExMPHRcfH09eXl6Z\n7SX7+svrwipcblwzWDT49FN2d+5MfgVj4GnShLOfeIK1Xbv6fXNUOI9poM7+8EP+160bxUeMYUVj\nenqDBhQOHswvN9xgd3jlqjttGr4zzmBniP3c9Tq1nsbUehpT62lMreXkeDqaEL/zzjt4PB6++OIL\n1q5dy6BBg8rUAefn55OUlERCQgL5+fllticmJpbZXrKvv1JTU617In7wer2OXzOobNtGvYwMKPXG\nplzXXUfq99+DHwlX2I9pIP4oTWnVoUOZzZWO6csvQ4cOJPft605v3zFj4LHHaNismfPXria9Tq2n\nMbWextR6GlNr2TGeFSXYjpZMvPnmm0yaNIns7GzOOeccxowZQ3p6Ojk5OQDMnz+fNm3a0Lx5c7xe\nLwUFBeTm5rJhwwZSUlJo3bo18+bNO7SvXnhBqrgY8vMrT4YB7roLXnnF/pjCXXXbqB13nOlK0bcv\n+HzWx1URnw/WrXO3hllERMKC601gBw0axAsvvMCNN95IUVERnTt3pm7duvTq1YusrCxuvfVWBgwY\nQGxsLJmZmXz77bdkZmYydepU/vrXv7odvpRn48bKOxmUaNLEdKNYu9bemMJdIH2FL77YzA5nZ1sb\nU2W+/RaaNlX9sIiI2M61rvbZpX65Tpo06ajvZ2RkkJGRUWZbXFwc48ePtz02CdDy5ZXfUFfaX/4C\nf/876Gdrj6Iik1yefXb1zzFmDKSnw+WXwymnWBdbRebNM9cUERGxmeszxFIDVTUhvuIKk/zs3Wtf\nTOEsJwcuvDCwmdakJLOss5Ofyqj/sIiIOEQJsVivqglxZCR07w7/+Y99MYWzWbPMm45Ade1qVq57\n553Az1UZnw9WroTzz7f/WiIiEvaUEIv1tm6F00+v2jG9e8PEifbEE+5mz4Y//cmacz33HDzxBNi9\nSuSWLZCcbN4siYiI2EwJsVhr50448cSqfzzfoIHpSrFmjT1xhauffjKdIqrQorBCJ54IQ4bA/fdb\nc75jmT9f9cMiIuIYJcRiLX9WqDsWrVxnvY8/hs6drT1njx7w+++mc4VddEOdiIg4SAmxWKuq9cOl\nde1qPt7fv9/amMJZIO3WjsXjgZdegocfhiqsFlklX38NrVvbc24REZEjKCEWay1fDi1aVO/YqCjo\n1g2mT7c2pnB18CCsWgXNm1t/7lNPhfvug0cesf7c27ZB3boQHW39uUVERMrhWh9iqaHWrQus3+3t\nt5sb7LKyrIspXHm9kJpq38IWt91mZqBbt4Y6dUySfMop5qv03xs3NnXM/lK7NRERcZgSYrHO/v0Q\nEWFWnquuM84wx//vf5CSYlloYcmqdmvH4vHA1KmmRdpvv8GOHbB9u/lzxw745hvzeNMmk+T6m5jP\nnw+ZmfbFLSIicgQlxGKdNWvg3HMDP0/JzXXPPBP4ucLZp59Cv372X8fjMTPEdeqU/+nAnXea5Lxr\nV//O99VXMG6ctTGKiIhUQDXEYp1A6odLu/pq0x2hoCDwc4Wrkj7BJ5zgbhwAgwebVe58vsr3/eUX\n036vVi374xIREfmDEmKxTiAdJkqLjoYrr4T33gv8XOFq4cLgqcM94wxo1szMEldmwQLo2NH2kERE\nREpTQizWWbHCmhligDvugNdes+Zc4WjhwuBKLAcPhiefrHyWWDfUiYiIC5QQizVKbqyqU8ea8zVp\nYs65YYM15ws3ixdDu3ZuR3HYGWeY+vIPP6x4vy++gAsvdCQkERGREkqIxRqbN5ukx0qaJa6effug\nsBBq13Y7krIqqyX+9VdTLhMf72xcIiIS9pQQizWsqh8u7dprYeZMKCqy9rw13ZIlkJbmdhRHq2yW\neNEi6NDB0ZBERERACbFYZcUK6xPi2Fjo3Bk++MDa89Z0CxcGb2L56KPHniWeNw/S052PSUREwp4S\n4lBVUAAjR7odxWF2zBCDKZt49VXrz1uTBXNC3LAhnHeemfk/0qJF0L698zGJiEjYU0IcqjZuhMcf\nN3WXwWDzZpPsWO2ss0zyv3mz9eeuiQ4ehG3boH59tyM5tvJqifPyTOzBVvcsIiJhQQlxqNqwwSxe\nUNld+07Ys8ckMv4uzVtVt98O//ynPeeuaVavNjOwwaxhQzj//LKzxF98EVxdMUREJKwoIQ5VGzea\nRDEYFq+wo364tOuvhxkz4MAB+65RUwRzuURpR84Sq/+wiIi4SAlxqNqwAa67DtatMy223GR3Qlyr\nFvTowRkjRkB+vn3XqQkWLAiNhLhklvi//zWPQyVuERGpkZQQh6oNG8ziFZ06weefuxvL8uXWrVB3\nLEOHktu6tXm+a9bYe61Q5fOZkolmzdyOxD8ls8T79pka4pNOcjsiEREJU0qIQ9W2bXDaaXDNNe6X\nTTiRhHk87OrWzdQS33ILvPGGvdcLRd9/D8nJEBEi/6wbNjRvpIYNg7Zt3Y5GRETCWIj85pQyiovN\nnxERpm/rggXHXv3LboWFJp5atZy53vnnmxnxTz+F3r1h715nrhsKFi6Ejh3djqJqHnkExo9X/2ER\nEXGVEuJQVDI7DGap2/PPh2XL3Inlu+8gJcXZayYkmBni9u3NjVgqoTBC5Ya60ho2NAnxZZe5HYmI\niIQxJcShaONGaNz48ONu3dwrm1i7Fs45x/nrejyH27EFWkLh85nEfsoUePdd62J02tKl0KaN21FU\n3V13wQknuB2FiIiEsSi3A5BqKLmhrkSXLjB2rFmow2lr1riTEJcoKaG4+26YOtXUpNavf/irQQNz\ns1bpHsk//wxLlsBXX5mvbdvMeKalwZtvQteuZtnoULJnD8TFhV7cIiIiQUAJcSjauLHsTUhJSXD8\n8bBliz2rxVVk7Vro3t3Zax4pIQGys00s338PW7fC11+bWfOtW+GXX8x+8fGQm2sS5LQ083XnnXD6\n6YfPtWePaQV2/fXuPJfqWrxYyx6LiIhUkxJiOxw4YO9Nbhs2QGZm2W3XXAPvvw/9+tl33fJ8+y00\nbersNcvj8ZhOFxV1u8jNNUlxRV0YbrnFtAMLtYQ4FOuHRUREgoRqiO3wwgvUmTPHvvNv3AiNGpXd\ndvXVJiF2UnGxSf5jYpy9bnUlJlbekuzcc01JRcmscqhYtAguusjtKEREREKSEmI7tG5N4tKl9p1/\n/35TL1pagwZmBvTXX+277pHcKNFwQlaWucEuVOzfb9rPHX+825GIiIiEJCXEdkhLI371anvO/fvv\npma4PF27wqxZ9ly3PG51mLBbz56hlRB7vXDBBW5HISIiErKUENshLo7i2FjYvdv6cx/ZYaI0p9uv\nrV0bOssEV0XdunDyyWYFvlCg+mEREZGAKCG2SV7LliZRsdqRPYhLa97cJKmFhdZftzw1dYYYQmt5\n6AULlBCLiIgEQAmxTfJatzaJitUqmiH2eMzKbfPmWX/d8qxbB2ef7cy1nHbllab85OBBtyOpWHGx\naTWXnOx2JCIiIiFLCbFN8s4/39z5b7WKEmJwrmzC54P8fNMDuCaKjYWOHWH2bLcjqdiaNaYzRumF\nR0RERKRKlBDbpDghwcze5eVZe+KKSiYA0tNh/nx7+yCDaU1Wr56913BbKJRNLFxoEncRERGpNiXE\ndrroIvjiC2vPuXOnWWntWKKj4bzzYPlya697pJpcP1wiLc3MwP7+u9uRHJtuqBMREQmYowlxUVER\nDz74IFlZWfTo0YPZs2ezZcsWMjMzycrKYvjw4RQXFwMwbdo0unfvTkZGBnPnzgVg//799OvXj6ys\nLPr06cNuO7o4WKlkttYqRUUQFVX5x+NOlE2EQ0Ls8UCPHvDOO25HcmyrVpmSCREREak2RxPi999/\nnzp16jB58mRee+01RowYwejRo+nfvz+TJ0/G5/Mxe/ZsfvnlF7Kzs3nrrbeYOHEi48aNo7CwkClT\nppCSksLkyZO59tprmTBhgpPhV12HDtZ2mvD35qkuXezvRxwOCTHAzTdDdrbbUZTvhx/gtNMgMtLt\nSEREREKaowlxly5duO+++wDw+XxERkayevVq0tLSAEhPT2fx4sWsXLmSVq1aERMTQ2JiIsnJyaxb\ntw6v10vHP+ol09PT+cLqcgSrnXSS+bi9oMCa81V2Q12J2rWhTh2TQNslXBLi5GSz3PPmzW5HcrRF\ni1QuISIiYoEoJy8WHx8PQF5eHvfeey/9+/dnzJgxeP4oAYiPjyc3N5e8vDwSExPLHJeXl1dme8m+\n/vJ6vRY+E/+vmdyoEbsmTSK/ZcuAz3fS3LkQGclOP55L3ZYt4cUX+eXGGwO+bnnO+f571n7/vb1J\ndznc+Dme0LEjMWPGsOOOOxy/dkUaTJ/OnssuIy/AMXFjTGs6jan1NKbW05haT2NqLSfH09GEGGD7\n9u3cc889ZGVlcfXVV/PMM88c+l5+fj5JSUkkJCSQn59fZntiYmKZ7SX7+is1NdW6J+EHr9drrnn9\n9dTdtAmsuP5bb8Hll9PQn3PVqwe3307y008Hft0j/fYbnHyye2PqtLPOgo4dOX3ChOBqb7Z5M/Vu\nvhni4qp9CtfGtAbTmFpPY2o9jan1NKbWsmM8K0qwHS2Z2LlzJ7179+bBBx+kR48eADRr1oycnBwA\n5s+fT5s2bWjevDler5eCggJyc3PZsGEDKSkptG7dmnl/LDoxf/780Hjhdexo3QId/pZMADRoYMo1\nfv3VmmuXVpMX5ChPQgKcfz58+aXbkRz2228QExNQMiwiIiKGozPEL7/8Mr///jsTJkw4dEPco48+\nysiRIxk3bhyNGzemc+fOREZG0qtXL7KysvD5fAwYMIDY2FgyMzMZNGgQmZmZREdHM3bsWCfDr576\n9WH7djhwwHSICMT335tE119XXAEffwxWl02ES/1wabfeCv/+N7Rr53YkxhdfmLZ+IiIiEjBHE+Ih\nQ4YwZMiQo7ZPmjTpqG0ZGRlkZGSU2RYXF8f48eNti882LVvCihWBlU34fGYZ4eho/4+58koYP96e\nhPhPf7L2nMGuUycYMAD274datdyORv2HRURELKSFOZxgRT/inTuhbt2qHdO6NSxbZlbMs1I4zhBH\nRsJVV8EHH7gdiZGTAxde6HYUIiIiNYISYidYkRBv2FDxks3liYiANm1gyZLArn2krVurVrpRUwTL\nUs4+H/zyS81fOltERMQhSoid0KSJSWh9vuqfoyo31JV25ZUwc2b1r3uk/fvNzVzB1G3BKWefDbt3\nw5Yt7saxeTOccYa7MYiIiNQgSoid4PGYEoO1a6t/jo0bqz5DDHDZZfDpp9W/7pG+/RaaNrXufKHm\n2Wfh2mvhu+/ci+Hrr005jIiIiFhCCbFTAi2bqO4Mce3apjXX9u3Vv3Zp4Vg/XFq7dvCvf8H115sb\nJd3g9VrT11pEREQAJcTOsSIhrs4MMUDXrjBrVvWvXVq4J8Rguoa8/bZpxbZ4sfPX93o1QywiImIh\nJcROOfdcWL26+nXEublQhZX5yrCyjlgJsZGSAv/9L9x7r+n17BSfD3bsgFNPde6aIiIiNZwSYqdE\nREDDhuaGqKraty+wFcnOPtvUvBYWVv8cJapbulET1a8PH30ETzwB06Y5c80ffoDkZGeuJSIiEiaU\nEDupumUT1b2hroTHY5aQXriw+ucAszBIcXHVFgep6U46ySTFr75qvuymcgkRERHLKSF2klsJMVhT\nNqF2X+VLTDQLdnz4ITz9tL3X+vpr3VAnIiJiMSXETmrVyiQ0VWVFmUKnTvD554GdQ/XDx1arlrnR\nbvVqePJJ+66jDhMiIiKWU0LspOho8xF7VVugbdwYeEIcF2duxNq4sfrnWLsWmjULLI6aLCrKtGSb\nMQN+/tn68/t88OOPcNpp1p9bREQkjCkhdlp6OixYULVjAmm5VlqgZRNr1miGuDIREfDXv8L48daf\ne9s2OP308FwlUERExEZKiJ1WnTribdusabPVtWtgCfH69XDWWYHHUdNlZpqa4txca8+rG+pERERs\noYTYaWlpkJPj//7FxWZGMMKCH1XDhrBzJ+TnV/1Yn8+0fzvuuMDjqOliYuC226zvOqEb6kRERGyh\nhNhpcXHma/du//b/8UfzMblV/vxnmDOn6sdt367FIKqiTx94/XVrej+X0A11IiIitlBC7IYOHWDR\nIv/2tXohjOrWEavDRNUkJEC3bvDmm9ad8/vvoUED684nIiIigBJid1SljtiKHsSltWsHixdXfQlp\nJcRVd++98NJLpuwlUNu3wymn6IY6ERERGyghdsNFF8Fnn8GBA5Xva/UMcXS0SWxXraracUqIq65u\nXbjwQnODXaBUPywiImIbJcRuSEoypQsTJ1a+r9UJMVSvbEIJcfUMHAhjx1Z9Rv5I6jAhIiJiGyXE\nbnn4YZgwAX77reL97FguuUsXmDWrasfs2QMnnGBtHOGgUSOoXx8WLgzsPJohFhERsY0SYrckJED/\n/pUv81tQYJYFtlK9eqZcY88e//b/9VeoXdvaGMLJQw/BmDGBnWPTJuvfGImIiAighNhdt95qVq3b\nsKH879uZiHbuDB9/7N++KpcITMuWcPAgfPNN9Y7/+WdTj6wb6kRERGyhhNhNERHw1FNmBrE8VneY\nKK0qq9YpIQ7cQw/B009X71iVS4iIiNhKCbHbLr7Y/Pn550d/z44b6kq0bg3Ll5uZy8qsXQvNmtkT\nR7jo1MmUPWzZUvVjdUOdiIiIrZQQB4Onn4ZHHjk6ObVzhjgiAi64AJYsqXxfzRAHzuOB+++HceOq\nfqxmiEVERGylhDgYNGkCHTvCv/9ddrudM8Tgf/u1bdvgtNPsiyNcdOtmasZ37qzacXa/DkRERMKc\nEuJg8eij8PzzkJt7eJvdidBll8GHH8Ldd8OKFeXvs2+f6XKhG7oCFxkJf/kLvPii/8fs2gXHH6/x\nFxERsZES4mBRu7ZJlp566vC2Xbvs7f2blGRKJrp3h8ceM0tKZ2fD/v2H91m/HlJS7Ish3PTqBdOn\nQ36+f/urXEJERMR2SoiDye23w6efmhuvCgshJsb+mcGICLj8cnj3XZg8Gb77DtLS4IEHzN9VP2yt\n2Fi46y4YNcq//XVDnYiIiO2UEAeTqCizUMegQSYpbtjQ2evXrw+PP26SsIsugnvuMYmxEmJr3X03\nzJ0L69ZVvq9miEVERGynhDjYXHYZ7N0LkybZ12GiMtHRpozi449h0SKz1LNYJzISnnsO+vUDn6/i\nfb/9Fpo2dSYuERGRMKWEOBg984xpxRYMnQXOOMOUboi10tLgzDNhypRj77Nnj6nzjtA/UxERETvp\nN20wOussGDbMJE1Sc40aBWPGmCW6y7NsmeqHRUREHKCEOFg98gi0bOl2FGKn44+HgQNh6NDyv+/1\nqn5YRETEAUqIRdzUqxesXm2S3yOpw4SIiIgjlBCLuMnjgRdegHvvPXrp7vXrTfmMiIiI2EoJsYjb\nzj0XOnSAV189vO233yAhwXSkEBEREVuFXEJcXFzMsGHDuPHGG+nVqxdbtmxxOySRwA0bBi+/DD/9\nZB4vWwatWrkbk4iISJgIuYT4s88+o7CwkKlTpzJw4ECeKr3UsUioio83y2c/9JB5rAU5REREHBNy\nCbHX66Vjx44AtGzZklWrVrkckYhFunUzvYfnzdMNdSIiIg7y+HyVLZUVXB599FEuv/xyLr74YgA6\nderEZ599RlRU1DGP8ZZ3B79IEIr58UcaP/IInqIi1mZnm+W8RURExBKpx/j0NeR+2yYkJJCfn3/o\ncXFxcYXJcIljDYBdvF6v49es6cJiTFNTYdUq+OADUtu2tf1yYTGmDtOYWk9jaj2NqfU0ptayYzwr\nmiANuZKJ1q1bM3/+fACWL19OSkqKyxGJWOyBB+C119yOQkREJGyE3AzxZZddxqJFi+jZsyc+n49R\no0a5HZKItWJiTCs2ERERcUTIJcQRERE88cQTbochIiIiIjVEyJVMiIiIiIhYSQmxiIiIiIQ1JcQi\nIiIiEtaUEIuIiIhIWFNCLCIiIiJhTQmxiIiIiIQ1JcQiIiIiEtaUEIuIiIhIWPP4fD6f20HYraK1\nq0VEREQkPKSmppa7PSwSYhERERGRY1HJhIiIiIiENSXEIiIiIhLWlBCLiIiISFhTQiwiIiIiYU0J\nsYiIiIiENSXEIiIiIhLWotwOoCYpLi7mscceY/369cTExDBy5EgaNmzodlgha8WKFTz77LNkZ2ez\nZcsWHn74YTweD02bNmX48OFEROj9nL+KiooYPHgwP/74I4WFhfTt25czzzxTYxqggwcPMmTIEDZt\n2oTH4+Hxxx8nNjZW4xqgXbt20b17d/75z38SFRWl8QzQddddR0JCAgD169fn7rvv1pgG6JVXXmHO\nnDkUFRWRmZlJWlqaxjQA06dP59133wWgoKCAtWvXMnnyZEaNGuXYmOqnZaHPPvuMwsJCpk6dysCB\nA3nqqafcDilkvfrqqwwZMoSCggIARo8eTf/+/Zk8eTI+n4/Zs2e7HGFoef/996lTpw6TJ0/mtdde\nY8SIERpTC8ydOxeAt956i/79+/O3v/1N4xqgoqIihg0bRq1atQD92w9UQUEBPp+P7OxssrOzGT16\ntMY0QDk5OSxbtowpU6aQnZ3Njh07NKYB6t69+6HX6LnnnsuQIUN46aWXHB1TJcQW8nq9dOzYEYCW\nLVuyatUqlyMKXcnJybzwwguHHq9evZq0tDQA0tPTWbx4sVuhhaQuXbpw3333AeDz+YiMjNSYWuDS\nSy9lxIgRAGzbto2kpCSNa4DGjBlDz549qVevHqB/+4Fat24d+/bto3fv3txyyy0sX75cYxqghQsX\nkpKSwj333MPdd99Np06dNKYW+eabb/juu++48cYbHR9TJcQWysvLO/SxFEBkZCQHDhxwMaLQ1blz\nZ6KiDlf0+Hw+PB4PAPHx8eTm5roVWkiKj48nISGBvLw87r33Xvr3768xtUhUVBSDBg1ixIgRXH31\n1RrXAEyfPp0TTjjh0MQC6N9+oGrVqsXtt9/OxIkTefzxx3nggQc0pgHas2cPq1at4vnnn9eYWuyV\nV17hnnvuAZz/t6+E2EIJCQnk5+cfelxcXFwmqZPqK103lJ+fT1JSkovRhKbt27dzyy230K1bN66+\n+mqNqYXGjBnDxx9/zNChQw+V+YDGtareeecdFi9eTK9evVi7di2DBg1i9+7dh76v8ay6Ro0acc01\n15VGNmsAACAASURBVODxeGjUqBF16tRh165dh76vMa26OnXq0KFDB2JiYmjcuDGxsbFlkjWNafX8\n/vvvbNq0iQsvvBBw/ve+EmILtW7dmvnz5wOwfPlyUlJSXI6o5mjWrBk5OTkAzJ8/nzZt2rgcUWjZ\nuXMnvXv35sEHH6RHjx6AxtQKM2bM4JVXXgEgLi4Oj8fDeeedp3GtpjfffJNJkyaRnZ3NOeecw5gx\nY0hPT9d4BuA///nPoftZfvrpJ/Ly8mjfvr3GNACpqaksWLAAn8/HTz/9xL59+2jXrp3GNEBLliyh\nXbt2hx47/TvK4/P5fLZeIYyUdJn43//+h8/nY9SoUTRp0sTtsELW1q1buf/++5k2bRqbNm1i6NCh\nFBUV0bhxY0aOHElkZKTbIYaMkSNHMmvWLBo3bnxo26OPPsrIkSM1pgHYu3cvjzzyCDt37uTAgQP0\n6dOHJk2a6LVqgV69evHYY48RERGh8QxAYWEhjzzyCNu2bcPj8fDAAw9w/PHHa0wD9PTTT5OTk4PP\n52PAgAHUr19fYxqg1157jaioKG677TYAx3/vKyEWERERkbCmkgkRERERCWtKiEVEREQkrCkhFhER\nEZGwpoRYRERERMKaEmIRERERCWtKiEVEREQkrCkhFhEREZGwpoRYRERERMKaEmIRERERCWtKiEVE\nREQkrCkhFhEREZGwpoRYRERERMKaEmIRERERCWtKiEVEREQkrEW5HUBVFRcX89hjj7F+/XpiYmIY\nOXIkDRs2dDssEREREQlRITdD/Nlnn1FYWMjUqVMZOHAgTz31lNshiYiIiEgIC7mE2Ov10rFjRwBa\ntmzJqlWrXI5IREREREJZyJVM5OXlkZCQcOhxZGQkBw4cICrq2E/F6/U6EZqIiIiIBLHU1NRyt4dc\nQpyQkEB+fv6hx8XFxRUmwyWONQBO8Hq9R13f87jnmPv7hvsq3Kfk+5UJ5Bol+4RTnHZeQ3EGb5xW\n8CcOu6/hxM/Mn3NUJljiDJbxrEywxBkK4xkq/6cES5zB8vosrbx8yUoVTZCGXMlE69atmT9/PgDL\nly8nJSXF5YhEREREJJSF3AzxZZddxqJFi+jZsyc+n49Ro0a5HZJIyCn9ztzud+QiIiLBLuQS4oiI\nCJ544gm3wxARERGRGiLkEmIRkWCjGXcRkdAWcjXEIiIiIiJWUkIsIiIiImFNCbGIiIiIhDXVEItI\njWZlv2EREamZlBCLiIiISJXVpAkHlUyIiIiISFhTQiwiIiIiYU0lEyIi4pia9BGriNQcmiEWERER\nkbCmhFhEREREwppKJiRg+ghUREREQplmiEVEREQkrCkhFhEREZGwpoRYRERERMKaEmIRERERCWu6\nqU5EQpZu6BQRESsoIRYREZGQ588bZL2JlmNRQiwiIiISZvTmoCwlxCIStPQftrhFrz2R8KKEWERE\nRAKiNxAS6pQQi4iIiIjlQumNktquiYiIiEhY0wyxiIiIiENCadY0nGiGWERERETCmmaIRUREqkEz\nfWIXvbacp4RYREREBCWi4UwlEyIiIiIS1jRDLCJH0SyJiIiEEyXEIiIhQG9SRIJD6X+LXq+X1NRU\nV2MIZB85TCUTIiIiIhLWlBCLiIiISFhTyYSIiIgN9JG1SOjQDLGIiIiIhDUlxCIiIiIS1lQyISJS\nA+jjeRGR6lNCLCISJpQ0i4iUTyUTIiIiIhLWNEMsIiISpDSrL+IMzRCLiIiISFjTDLG4TjMgIiIi\n4ibNEIuIiIhIWNMMsYiIBJXSnxp5vV5SU1NdjEZEwoErM8SffvopAwcOPPR4+fLl3HDDDfTs2ZMX\nX3zx0PYXX3yRHj160LNnT1auXAnA7t276d27N1lZWfTv3599+/Y5Hr+IiIiI1ByOJ8QjR45k7Nix\nFBcXH9o2fPhwxo4dy5QpU1ixYgVr1qxh9erVfPXVV7z99tuMGzeOxx9/HIAJEyZw1VVXMXnyZJo1\na8bUqVOdfgoiIiIiUoM4XjLRunVrLr300kOJbF5eHoWFhSQnJwPQoUMHFi9eTExMDB06dMDj8XDa\naadx8OBBdu/ejdfr5a677gIgPT2dcePGcdtttzn9NEREahzd4Coi4cq2hPjtt9/m3//+d5lto0aN\nomvXruTk5BzalpeXR0JCwqHH8fHx/PDDD8TGxlKnTp0y23Nzc8nLyyMxMbHMNn94vd5Ank7AqnL9\nyva14rn4cw4n4rBCoHE6NRbBHKeVP8ua9NqqrlCPvzrsfM5O//9ZU35+wfD/VomlVy219Rpu/sxq\nyuslWLg1nrYlxDfccAM33HBDpfslJCSQn59/6HH+/7N391F21fW9+N9DkklCJmmIiPeiBgxXBHVB\nyGShrIaAoMVSIhCIASq5CLbAgshjDCAEkBikNigPBUGBKleQRKwt7VJuASEGCuLRYEFBRAwPQZ4C\nl8zwkGTm/P7ozymBMMzDOXNm8n291mKtOXvv2fvz/eyHeWezzznt7Rk3blxGjBjxpuljx47tWn7U\nqFFdy/ZEI9+UsdE3hfzrWy/ftexbLNPjsfRjG13L9GQdA6EWdb5dP+u4jUFX5+v0+U1LA1znULHJ\nvwmsv9elXqrb9XNTOf4GqhcDsd+H4D7b5M/3AVbvfnYXthv+sWstLS0ZMWJEHnvssVSr1SxfvjxT\np07NlClTsnz58nR2dmbVqlXp7OzMhAkTMmXKlNxxxx1JkmXLljkQAQDol0HxsWvnnntuTj311HR0\ndGTatGnZeeedkyRTp07N7Nmz09nZmQULFiRJjj322MyfPz9LlizJFltskcWLFzeydAAAhriGBOKP\nfOQj+chHPtL1evLkyVmyZMmblps7d27mzp27wbQtt9wyV111Vd1rBAAGjjd10kgNf2QCAAAaaVA8\nMgEADF7u3rKpE4gBgEFPKKeePDIBAEDR3CHehPnXNLApcm37bz3pxeuX8bm5sHECMdAnQgkAmwqB\nGIAe8w+hTY99CgIxUCf+yAIwVHhTHQAARROIAQAomkAMAEDRBGIAAIrmTXWwifFmNgDoHXeIAQAo\nmkAMAEDRBGIAAIomEAMAUDRvqgNgk+PNpUBvuEMMAEDRBGIAAIomEAMAUDTPEDeI59sAAAYHd4gB\nACiaQAwAQNEEYgAAiiYQAwBQNIEYAICiCcQAABTNx64BUBwffQm8njvEAAAUTSAGAKBoAjEAAEUT\niAEAKJpADABA0QRiAACK5mPXGBJe/xFJlUolra2tDawGANiUuEMMAEDRBGIAAIomEAMAUDSBGACA\nognEAAAUTSAGAKBoAjEAAEUb0M8hXrNmTebNm5e2trasW7cup512WnbZZZesWLEiX/7ylzNs2LBM\nmzYtxx9/fJLk0ksvze23357hw4fnjDPOyE477ZTVq1fn1FNPzauvvpqtttoq559/fkaPHj2QwwAA\nYBMyoIH4mmuuyUc/+tEcccQR+f3vf59TTjkl//RP/5Szzz47l1xySd773vfmb//2b/PrX/861Wo1\nP/vZz7J06dI89dRTmTt3bm688cZcdtll2W+//TJz5sxceeWVueGGG3LEEUcM5DCAGnj9l60AQCMN\naCA+4ogj0tzcnCTp6OjIyJEj09bWlrVr12bixIlJkmnTpuWuu+5Kc3Nzpk2blqampmy99dbp6OjI\n6tWrU6lUcvTRRydJpk+fngsvvLBHgbhSqdRtXD1Ry+0P1FjebjuN7Glvtl2LcQyGddRiG7X+PTZO\nP2tLP2tLP2tLP2urUf2sWyBeunRpvv3tb28wbdGiRdlpp53y7LPPZt68eTnjjDPS1taWlpaWrmXG\njBmTxx9/PCNHjsz48eM3mL5mzZq0tbVl7NixG0zriUZ+1W+fv2r4Xzc+uaZjeYttdG3n7eY3wEb7\n2ZM6366ftehFHdfR4230kq/Cri39rC39rC39rC39rK1697O7sF23QDxr1qzMmjXrTdMfeuihnHzy\nyfnCF76QXXfdNW1tbWlvb++a397ennHjxmXEiBFvmj527Ni0tLSkvb09o0aN6loWAAD6akAfmfjd\n736XE044IV//+tezww47JElaWloyYsSIPPbYY3nve9+b5cuX5/jjj8+wYcPy1a9+NUcddVT++Mc/\nprOzMxMmTMiUKVNyxx13ZObMmVm2bJl/mVEznmkFgDINaCBevHhx1q5dmy9/+ctJ/isMX3755Tn3\n3HNz6qmnpqOjI9OmTcvOO++cJJk6dWpmz56dzs7OLFiwIEly7LHHZv78+VmyZEm22GKLLF68eCCH\nwCAlzAIAfTWggfjyyy/f6PTJkydnyZIlb5o+d+7czJ07d4NpW265Za666qq61AcAQHkGNBBDI7mL\nDABsjG+qAwCgaO4QD2LuaAIA1J87xAAAFE0gBgCgaAIxAABFE4gBACiaQAwAQNEEYgAAiiYQAwBQ\nNIEYAICiCcQAABRNIAYAoGgCMQAARRvek4V++9vf5ve//31GjRqV7bbbLu9973vrXRcAAAyIbgPx\n888/n89//vN5+OGHs80226SpqSmPPvpoJk+enMWLF2fcuHEDVScAANRFt49MnHfeeWltbc2dd96Z\npUuXZsmSJbnzzjuzww47ZNGiRQNVIwAA1E23d4gfeuihfP3rX99gWnNzc04++eTsv//+dS2MgVE9\nu9roEgAAGqrbO8QjR47c6PSmpqZstpn34wEAMPR1m2qbmpr6NA8AAIaKbh+ZePjhh7P33nu/aXq1\nWs2zzz5bt6IAAGCgdBuIb7755iRJW1tbfvrTn2b06NGZPn26xyUAANhkdBuIR48enblz5+Z3v/td\nJk6cmKamplx00UWZPHly/v7v/36gagQAgLrp9lbvl770pbS2tmb58uVdH7u2fPnyfOADH/CxawAA\nbBJ87Brd8rFsAMCmzseuAQBQNB+7BgBA0XzsGgAARevRx64BAMCmqttA/O53v3ug6gAAgIbwzjgA\nAIomEAMAUDSBGACAognEAAAUTSAGAKBoAjEAAEUTiAEAKJpADABA0QRiAACKJhADAFC0br+6GRh8\nqmdXG10CAGxS3CEGAKBoAjEAAEUb0EcmXn755Zxyyil56aWXMmLEiFxwwQV517velRUrVuTLX/5y\nhg0blmnTpuX4449Pklx66aW5/fbbM3z48JxxxhnZaaedsnr16px66ql59dVXs9VWW+X888/P6NGj\nB3IYAABsQgb0DvGSJUvyoQ99KN/97nfzqU99Kt/85jeTJGeffXYWL16c66+/Pvfdd19+/etf54EH\nHsjPfvazLF26NBdeeGHOPffcJMlll12W/fbbL9ddd10++MEP5oYbbhjIIQAAsIkZ0DvERxxxRDo6\nOpIkq1atyrhx49LW1pa1a9dm4sSJSZJp06blrrvuSnNzc6ZNm5ampqZsvfXW6ejoyOrVq1OpVHL0\n0UcnSaZPn54LL7wwRxxxxNtuu1Kp1G1cPdHo7W9qBls/e1LP2y3T3/n9Mdj6OdTpZ23pZ23pZ23p\nZ201qp91C8RLly7Nt7/97Q2mLVq0KDvttFPmzJmT3/72t7nmmmvS1taWlpaWrmXGjBmTxx9/PCNH\njsz48eM3mL5mzZq0tbVl7NixG0zridbW1hqMqm8qlUpDt7+paVg///WtZ3XV83bL9GMd9Rqz47O2\n9LO29LO29LO29LO26t3P7sJ23QLxrFmzMmvWrI3O+853vpNHHnkkRx99dH74wx+mvb29a157e3vG\njRuXESNGvGn62LFj09LSkvb29owaNaprWRgIPu4MADZNA/oM8RVXXJEf/vCHSf7r7u6wYcPS0tKS\nESNG5LHHHku1Ws3y5cszderUTJkyJcuXL09nZ2dWrVqVzs7OTJgwIVOmTMkdd9yRJFm2bJl/mQEA\n0C8D+gzxQQcdlPnz5+fGG29MR0dHFi1alCQ599xzc+qpp6ajoyPTpk3LzjvvnCSZOnVqZs+enc7O\nzixYsCBJcuyxx2b+/PlZsmRJtthiiyxevHgghwAAwCZmQAPxlltumauuuupN0ydPnpwlS5a8afrc\nuXMzd+7cHq0DAAD6whdzAABQNIEYAICiCcQAABRNIAYAoGgCMQAARROIAQAomkAMAEDRBGIAAIom\nEAMAUDSBGACAognEAAAUTSAGAKBoAjEAAEUb3ugCYFNSPbva6BIAgF5yhxgAgKIJxAAAFE0gBgCg\naAIxAABFE4gBACiaQAwAQNEEYgAAiiYQAwBQNIEYAICiCcQAABRNIAYAoGgCMQAARROIAQAomkAM\nAEDRBGIAAIomEAMAUDSBGACAognEAAAUTSAGAKBoAjEAAEUTiAEAKJpADABA0QRiAACKJhADAFA0\ngRgAgKIJxAAAFE0gBgCgaAIxAABFa0ggfuSRR9La2prXXnstSbJixYrMmjUrhxxySC699NKu5S69\n9NIcfPDBOeSQQ/KrX/0qSbJ69eoceeSROeyww3LiiSfmlVdeacQQAADYRAx4IG5ra8sFF1yQ5ubm\nrmlnn312Fi9enOuvvz733Xdffv3rX+eBBx7Iz372syxdujQXXnhhzj333CTJZZddlv322y/XXXdd\nPvjBD+aGG24Y6CEAALAJGdBAXK1Wc9ZZZ+Xkk0/O6NGjk/xXQF67dm0mTpyYpqamTJs2LXfddVcq\nlUqmTZuWpqambL311uno6Mjq1atTqVSy++67J0mmT5+eu+66ayCHAADAJmZ4vVa8dOnSfPvb395g\n2tZbb5199903O+ywQ9e0tra2tLS0dL0eM2ZMHn/88YwcOTLjx4/fYPqaNWvS1taWsWPHbjCtJyqV\nSn+G02+N3v6mZlPs59uNqZ5j3hT72Uj6WVv6WVv6WVv6WVuN6mfdAvGsWbMya9asDaZ94hOfyI03\n3pgbb7wxzz77bI488shcccUVaW9v71qmvb0948aNy4gRI940fezYsWlpaUl7e3tGjRrVtWxPtLa2\n1mZgfVCpVBq6/U3NkO7nv771rK4xvcUy9RrzkO7nIKSftaWftaWftaWftVXvfnYXtgf0kYl///d/\nz7XXXptrr70273znO3P11VenpaUlI0aMyGOPPZZqtZrly5dn6tSpmTJlSpYvX57Ozs6sWrUqnZ2d\nmTBhQqZMmZI77rgjSbJs2TIHIgAA/VK3O8S9ce655+bUU09NR0dHpk2blp133jlJMnXq1MyePTud\nnZ1ZsGBBkuTYY4/N/Pnzs2TJkmyxxRZZvHhxI0sHAGCIa1ggvu2227p+njx5cpYsWfKmZebOnZu5\nc+duMG3LLbfMVVddVff6AAAogy/mAACgaAIxAABFE4gBACjaoHhTHZSiena10SUAAG/gDjEAAEUT\niAEAKJpADABA0QRiAACKJhADAFA0gRgAgKIJxAAAFE0gBgCgaAIxAABFE4gBACiaQAwAQNEEYgAA\niiYQAwBQNIEYAICiCcQAABRNIAYAoGgCMQAARROIAQAomkAMAEDRBGIAAIomEAMAUDSBGACAognE\nAAAUTSAGAKBoAjEAAEUTiAEAKJpADABA0QRiAACKJhADAFC04Y0uANhQ9exqo0sAgKK4QwwAQNEE\nYgAAiiYQAwBQNIEYAICiCcQAABRNIAYAoGgCMQAARROIAQAomkAMAEDRBGIAAIomEAMAULSmarVa\nbXQR9VapVBpdAgAADdba2rrR6UUEYgAAeCsemQAAoGgCMQAARROIAQAomkAMAEDRBGIAAIomEAMA\nULThjS5gU9bZ2ZlzzjknDz30UJqbm7Nw4cJss802jS5rSLrvvvvy93//97n22muzcuXKnHbaaWlq\nasr73//+nH322dlsM/+264l169bljDPOyJNPPpm1a9fm2GOPzf/6X/9LP/uoo6MjZ555Zh599NE0\nNTXl3HPPzciRI/Wzn55//vnMnDkzV199dYYPH66f/XDggQempaUlSfKe97wnxxxzjH72wxVXXJHb\nbrst69aty6GHHppdd91VP/voBz/4Qf7pn/4pSfLaa6/lN7/5Ta677rosWrSoIf201+rolltuydq1\na3PDDTfklFNOyVe+8pVGlzQkffOb38yZZ56Z1157LUly/vnn58QTT8x1112XarWaW2+9tcEVDh3/\n8i//kvHjx+e6667Lt771rZx33nn62Q8/+clPkiTf+973cuKJJ+ZrX/uafvbTunXrsmDBgowaNSqJ\n870/XnvttVSr1Vx77bW59tprc/755+tnP9xzzz355S9/meuvvz7XXntt/vjHP+pnP8ycObPr2PzQ\nhz6UM888M//wD//QsH4KxHVUqVSy++67J0kmT56c+++/v8EVDU0TJ07MJZdc0vX6gQceyK677pok\nmT59eu66665GlTbkfPKTn8wJJ5yQJKlWqxk2bJh+9sPHP/7xnHfeeUmSVatWZdy4cfrZTxdccEEO\nOeSQbLXVVkmc7/3x4IMP5pVXXsmRRx6ZOXPmZMWKFfrZD8uXL8/222+f4447Lsccc0z23HNP/ayB\n//zP/8zvfve7zJ49u6H9FIjrqK2tret/VSXJsGHDsn79+gZWNDTts88+GT78v5/uqVaraWpqSpKM\nGTMma9asaVRpQ86YMWPS0tKStra2fP7zn8+JJ56on/00fPjwzJ8/P+edd15mzJihn/3wgx/8IBMm\nTOi6kZA43/tj1KhROeqoo3LVVVfl3HPPzamnnqqf/fDCCy/k/vvvz0UXXaSfNXTFFVfkuOOOS9LY\n810grqOWlpa0t7d3ve7s7Nwg2NE3r3+eqL29PePGjWtgNUPPU089lTlz5mT//ffPjBkz9LMGLrjg\ngtx8880566yzuh7tSfSzt2688cbcddddOfzww/Ob3/wm8+fPz+rVq7vm62fvvO9978unPvWpNDU1\n5X3ve1/Gjx+f559/vmu+fvbO+PHjM23atDQ3N2fSpEkZOXLkBoFNP3vvpZdeyqOPPpqPfvSjSRr7\n910grqMpU6Zk2bJlSZIVK1Zk++23b3BFm4YPfvCDueeee5Iky5Yty9SpUxtc0dDx3HPP5cgjj8y8\nefNy8MEHJ9HP/vjhD3+YK664IkkyevToNDU15cMf/rB+9tF3v/vd/J//839y7bXXZscdd8wFF1yQ\n6dOn62cfff/73+9678rTTz+dtra2/Pmf/7l+9lFra2t++tOfplqt5umnn84rr7yS3XbbTT/74d57\n781uu+3W9bqRf4+aqtVqdcC2Vpg/fcrEb3/721Sr1SxatCjbbbddo8sakp544omcfPLJWbJkSR59\n9NGcddZZWbduXSZNmpSFCxdm2LBhjS5xSFi4cGF+9KMfZdKkSV3TvvjFL2bhwoX62Qcvv/xyTj/9\n9Dz33HNZv359/uZv/ibbbbed47MGDj/88JxzzjnZbLPN9LOP1q5dm9NPPz2rVq1KU1NTTj311Gyx\nxRb62Q9/93d/l3vuuSfVajUnnXRS3vOe9+hnP3zrW9/K8OHDc8QRRyRJQ/++C8QAABTNIxMAABRN\nIAYAoGgCMQAARROIAQAomkAMAEDRBGIAAIomEAMAUDSBGACAognEAAAUTSAGAKBoAjEAAEUTiAEA\nKJpADABA0QRiAACKJhADAFA0gRgAgKIJxAAAFG14owsYCJVKpdElAADQYK2trRudXkQgTt66AQy8\nSqVif9AjjhV6w/FCTzlWytTdDVKPTAAAUDSBGACAognEAAAUTSAGAKBoAjEAAEUTiAEAKJpADABA\n0QRiAACKJhADAFA0gRgAgKIV89XNwODQdPvtvVq+Wp8yAKCLO8QAABRNIAYAoGgCMQAARROIAQAo\nmkAMAEDRBGIAAIomEAMAUDSBGACAognEAAAUTSAGAKBoAjEAAEUTiAEAKJpADABA0QRiAACKJhAD\nAFA0gRgAgKIJxAAAFE0gBgCgaAIxAABFE4gBACiaQAwAQNEEYgAAiiYQAwBQtOGNLqC31q1bl9NO\nOy1PPvlkNttss5x33nnZbrvtGl0WAABD1JC7Q3zHHXdk/fr1+d73vpfjjjsuX//61xtdEgAAQ9iQ\nu0P8vve9Lx0dHens7ExbW1uGD+/ZECqVSp0rozfsD3rKsUJvOF7oKccKrzfkAvHmm2+eJ598Mn/5\nl3+ZF154Id/4xjd69Hutra11royeqlQq9kfJbr+9V4s7Vugp1xZ6yrFSpu7+ETTkHpn4x3/8x0yb\nNi0333xz/vmf/zmnnXZaXnvttUaXBQDAEDXk7hCPGzcuI0aMSJL82Z/9WdavX5+Ojo4GVwUAwFA1\n5ALxEUcckTPOOCOHHXZY1q1bl5NOOimbb755o8sCAGCIGnKBeMyYMbnooosaXQYAAJuIIfcMMQAA\n1JJADABA0QRiAACKJhADAFA0gRgAgKIJxAAAFE0gBgCgaAIxAABFE4gBACiaQAwAQNEEYgAAiiYQ\nAwBQNIEYAICiCcQAABRNIAYAoGgCMQAARROIAQAomkAMAEDRBGIAAIomEAMAUDSBGACAognEAAAU\nTSAGAKBoAjEAAEUTiAEAKJpADABA0QRiAACKJhADAFA0gRgAgKIJxAAAFE0gBgCgaAIxAABFE4gB\nACiaQAwAQNEEYgAAiiYQAwBQNIEYAICiCcQAABRNIAYAoGgCMQAARROIAQAomkAMAEDRBGIAAIom\nEAMAUDSBGACAog1vdAF9ccUVV+S2227LunXrcuihh2bWrFmNLgkAgCFqyAXie+65J7/85S9z/fXX\n55VXXsnVV1/d6JIAABjChlwgXr58ebbffvscd9xxaWtryxe+8IVGlwQAwBDWVK1Wq40uojfOPPPM\nrFq1Kt/4xjfyxBNP5Nhjj82Pf/zjNDU1veXvVCqVAawQhr6pa9Y0uoQuPx87ttElALCJaG1t3ej0\nIXeHePz48Zk0aVKam5szadKkjBw5MqtXr8473vGObn/vrRrAwKtUKvbHYHf77Y2uoItjhZ5ybaGn\nHCtl6u4G6ZD7lInW1tb89Kc/TbVazdNPP51XXnkl48ePb3RZAAAMUUPuDvHHPvax3HvvvTn4TVlI\n3gAAIABJREFU4INTrVazYMGCDBs2rNFlAQAwRA25QJzEG+kAAKiZIffIBAAA1JJADABA0QRiAACK\nJhADAFA0gRgAgKIJxAAAFE0gBgCgaAIxAABFE4gBACiaQAwAQNEEYgAAiiYQAwBQNIEYAICiCcQA\nABRNIAYAoGgCMQAARROIAQAomkAMAEDRBGIAAIomEAMAUDSBGACAognEAAAUbXijC4Choun223u1\nfHXPPetSR2n0HYB6c4cYAICiCcQAABStYY9M7LDDDmlqakqSVKvVDeY1NTXlN7/5TSPKAgCgMA0L\nxA8++GCjNg0AAF0a/qa6l156KTfddFNefPHFDe4UH3/88Q2sCgCAUjQ8EJ9wwgkZO3Zs3v/+93c9\nQgEAAAOl4YH4ueeeyzXXXNPoMgAAKFTDP2Vixx139DwxAAAN0/A7xA8//HBmzpyZCRMmZOTIkV3T\nb7311gZWBQBAKRoeiC+88MLccccdufvuuzNs2LDsscce2W233RpdFgAAhWh4IP7GN76R1157LZ/+\n9KfT2dmZf/7nf87DDz+cL37xi40uDQCAAjQ8EN9333358Y9/3PV6r732yn777dfAigAAKEnD31T3\nP//n/8zKlSu7Xj/33HN517ve1cCKAAAoScPvEK9fvz77779/pk6dmuHDh6dSqeSd73xn5syZkyT5\nzne+0+AKAQDYlDU8EM+dO3eD10ceeWSDKgEAoEQND8S77rpro0sAAKBgDX+GGAAAGkkgBgCgaAIx\nAABFE4gBACiaQAwAQNEEYgAAiiYQAwBQtCEbiJ9//vnsscceeeSRRxpdCgAAQ9iQDMTr1q3LggUL\nMmrUqEaXAgDAEDckA/EFF1yQQw45JFtttVWjSwEAYIhr+Fc399YPfvCDTJgwIbvvvnuuvPLKHv9e\npVKpY1X0Vgn7YzCNceqaNY0uYcAMpr4z8Ox/esqxwus1VavVaqOL6I2//uu/TlNTU5qamvKb3/wm\n2267bS6//PK8853vfMvfqVQqaW1tHcAq6c5Q3R9Nt9/eq+Wre+5Zlzr6ore1D2WDqe8MrKF6bWHg\nOVbK1N1+H3J3iL/73e92/Xz44YfnnHPO6TYMAwBAd4bkM8QAAFArQ+4O8etde+21jS4BAIAhzh1i\nAACKJhADAFA0gRgAgKIJxAAAFE0gBgCgaAIxAABFE4gBACiaQAwAQNEEYgAAiiYQAwBQNIEYAICi\nCcQAABRNIAYAoGgCMQAARROIAQAomkAMAEDRBGIAAIomEAMAUDSBGACAognEAAAUTSAGAKBowxtd\nAHSn6fbbe7V8dc8961JHXwzl2gGgJO4QAwBQNIEYAICiCcQAABRNIAYAoGgCMQAARROIAQAomkAM\nAEDRBGIAAIomEAMAUDSBGACAognEAAAUTSAGAKBoAjEAAEUTiAEAKJpADABA0QRiAACKJhADAFA0\ngRgAgKIJxAAAFE0gBgCgaAIxAABFE4gBACiaQAwAQNEEYgAAija80QX01rp163LGGWfkySefzNq1\na3Psscdm7733bnRZAAAMUUMuEP/Lv/xLxo8fn69+9at58cUXc8ABBwjEAAD0WVO1Wq02uojeaG9v\nT7VaTUtLS1544YUcfPDBufXWW7v9nUqlMkDVDU5T16zp1fI/Hzu2TpX03mCqvbe19FZvaq93LUNZ\nPfs4mI6vwXSe8tbqea729hgYTMfYYKqFsrS2tm50+pC7QzxmzJgkSVtbWz7/+c/nxBNP7NHvvVUD\ninD77b1avN69qlQqPd/GYKq9l7X0Vq9qr3MtQ1k9+ziYjq+ir2lvoVfXloFSx3O112MdTMdYg2sZ\nlMcKddfdDdIh+aa6p556KnPmzMn++++fGTNmNLocAACGsCF3h/i5557LkUcemQULFmS33XZrdDkA\nAAxxQ+4O8Te+8Y289NJLueyyy3L44Yfn8MMPz6uvvtrosgAAGKKG3B3iM888M2eeeWajywAAYBMx\n5O4QAwBALQnEAAAUTSAGAKBoAjEAAEUTiAEAKJpADABA0QRiAACKJhADAFA0gRgAgKIJxAAAFE0g\nBgCgaAIxAABFE4gBACiaQAwAQNEEYgAAiiYQAwBQNIEYAICiCcQAABRNIAYAoGgCMQAARROIAQAo\n2vBGF7Cparr99l4tX91zz7rUMdh09aWX/en1+imWY2Dj6tmXoXz9GsrXasf6xvW4L///cvXep73Z\nT4OplqR39QzlcylxhxgAgMIJxAAAFE0gBgCgaAIxAABFE4gBACiaQAwAQNEEYgAAiiYQAwBQNIEY\nAICiCcQAABRNIAYAoGgCMQAARROIAQAomkAMAEDRBGIAAIomEAMAUDSBGACAognEAAAUTSAGAKBo\nAjEAAEUTiAEAKJpADABA0QRiAACKNrzRBfRWZ2dnzjnnnDz00ENpbm7OwoULs8022zS6LAAAhqgh\nd4f4lltuydq1a3PDDTfklFNOyVe+8pVGlwQAwBA25AJxpVLJ7rvvniSZPHly7r///gZXBADAUNZU\nrVarjS6iN774xS/mL/7iL7LHHnskSfbcc8/ccsstGT78rZ/+qFQqA1UeAACDVGtr60anD7lniFta\nWtLe3t71urOzs9swnLz14AEAYMg9MjFlypQsW7YsSbJixYpsv/32Da4IAIChbMg9MvGnT5n47W9/\nm2q1mkWLFmW77bZrdFkAAAxRQy4QAwBALQ25RyYAAKCWBGIAAIomEAMAUDSBmLr493//95xyyild\nr1esWJFZs2blkEMOyaWXXto1/dJLL83BBx+cQw45JL/61a+SJKtXr86RRx6Zww47LCeeeGJeeeWV\nAa+fxurs7MyCBQsye/bsHH744Vm5cmWjS6LB7rvvvhx++OFJkpUrV+bQQw/NYYcdlrPPPjudnZ1J\nkiVLlmTmzJn59Kc/nZ/85CdJkldffTVz587NYYcdlr/5m7/J6tWrGzYG6mvdunWZN29eDjvssBx8\n8MG59dZbHSv0XBVq7Lzzzqvus88+1RNPPLFr2qc+9anqypUrq52dndXPfe5z1QceeKB6//33Vw8/\n/PBqZ2dn9cknn6zOnDmz6/dvvPHGarVarV5xxRXVa665phHDoIFuvvnm6vz586vVarX6y1/+snrM\nMcc0uCIa6corr6zut99+1VmzZlWr1Wr16KOPrt59993VarVaPeuss6r/9//+3+ozzzxT3W+//aqv\nvfZa9aWXXur6+eqrr65efPHF1Wq1Wv3Xf/3X6nnnndewcVBf3//+96sLFy6sVqvV6gsvvFDdY489\nHCv0mDvE1NyUKVNyzjnndL1ua2vL2rVrM3HixDQ1NWXatGm56667UqlUMm3atDQ1NWXrrbdOR0dH\nVq9evcHXc0+fPj133XVXg0ZCo/iKdl5v4sSJueSSS7peP/DAA9l1112T/Pc14le/+lV22WWXNDc3\nZ+zYsZk4cWIefPDBN11P/uM//qMhY6D+PvnJT+aEE05IklSr1QwbNsyxQo8JxPTZ0qVLs99++23w\n369+9avsu+++aWpq6lqura0tLS0tXa/HjBmTNWvWdDt97NixG0yjLG88NoYNG5b169c3sCIaaZ99\n9tngG0mr1WrXNWZj140/TW9ra3M9KciYMWPS0tKStra2fP7zn8+JJ57oWKHHhtxXNzN4zJo1K7Nm\nzXrb5d74ddvt7e0ZN25cRowY8abpY8eO7Vp+1KhRXctSlr58RTvl2Gyz/76X86drxMauM6+/nrx+\nWTZdTz31VI477rgcdthhmTFjRr761a92zXOs0B13iKm7lpaWjBgxIo899liq1WqWL1+eqVOnZsqU\nKVm+fHk6OzuzatWqdHZ2ZsKECZkyZUruuOOOJMmyZcvS2tra4BEw0HxFO9354Ac/mHvuuSfJf10j\npk6dmp122imVSiWvvfZa1qxZk0ceeSTbb7+960lBnnvuuRx55JGZN29eDj744CSOFXrON9VRF/fc\nc0++973v5Wtf+1qS/wo1ixYtSkdHR6ZNm5aTTjopSXLJJZdk2bJl6ezszOmnn56pU6fmueeey/z5\n89Pe3p4tttgiixcvzuabb97I4TDAfEU7b/TEE0/k5JNPzpIlS/Loo4/mrLPOyrp16zJp0qQsXLgw\nw4YNy5IlS3LDDTekWq3m6KOPzj777JNXXnkl8+fPz7PPPpsRI0Zk8eLFeec739no4VAHCxcuzI9+\n9KNMmjSpa9oXv/jFLFy40LHC2xKIAQAomkcmAAAomkAMAEDRBGIAAIomEAMAUDSBGACAognEAAAU\nTSAGAKBoAjEAAEUTiAEAKJpADABA0QRiAACKJhADAFA0gRgAgKIJxAAAFE0gBgCgaAIxAABFG97o\nAgZCpVJpdAkAADRYa2vrRqcXEYiTt25AvVQqlQHf5kDZlMeWGN9QtimPLTG+oWxTHluyaY9vUx5b\nsumP7/W6u0HqkQkAAIomEAMAUDSBGACAognEAAAUTSAGAKBoAjEAAEUTiAEAKJpADABA0QRiAACK\nJhADAFA0gRgAgKIJxAAAFE0gBgCgaAIxAABFE4gBACiaQAwAQNEEYgAAijZoA/F9992Xww8//E3T\nb7vtthx00EGZPXt2lixZ0oDKAADYlAzKQPzNb34zZ555Zl577bUNpq9bty7nn39+rr766lx77bW5\n4YYb8txzzzWoyrc2b968zJgxI9tuu23Xf/Pmzdtg/uvn9XSZ18+vZa29reOiiy6qeR0DpRZ97UvP\narGNeh0DA2Gg+v7G86632xmovg/U+d2XOjblng3UeGtx3ezJeAfivOrpOrr7mzeQddRjHbXYd404\nx2tVx2DZN/XWVK1Wq40u4o1uvvnmfOADH8gXvvCFDe4CP/jgg/nqV7+aq666KkmyaNGi7LLLLvnL\nv/zLbtdXqVTqWu8bzZgxI88880y22mqrJOn6+aabbtro/J4s88b5jaq1XnUMlFqMZyB61pNjZChp\nRN/7sp2B6vtgOa9K69lAjXcgjveB3I51DM3rd63qqFdPPv7xj+eEE07o8TpqpbW1daPThw9wHT2y\nzz775IknnnjT9La2towdO7br9ZgxY9LW1tajdb5VA+qhubk5W221VVatWpUk2XbbbTeoobm5Oe95\nz3vyhz/8oet33m6ZN86vZa3dbWdj89euXTug/aylnvS1Uql0O76+9OyN2+htnX1dz8a83fjqoR49\n2VjfX3/e9WU79ex7d9vp6TZqve9qMd5a9qy78Q2W86qv1+/eXjd7Mt6BOK96uo7u/uYNZB31WEd/\n911fa+mvntbR3795fa2lEbq7QTooH5l4Ky0tLWlvb+963d7evkFABgCA3hpSgXi77bbLypUr8+KL\nL2bt2rX5+c9/nl122aXRZQEAMIQNykcm3uimm27Kyy+/nNmzZ+e0007LUUcdlWq1moMOOijvete7\nGl0eAABD2KANxO95z3u63lA3Y8aMrul77bVX9tprr0aVBQDAJmZIPTIBAAC1JhADAFA0gRgAgKIJ\nxAAAFE0gBgCgaAIxAABFE4gBACiaQAwAQNEEYgAAiiYQAwBQNIEYAICiCcQAABRNIAYAoGgCMQAA\nRROIAQAomkAMAEDRBGIAAIomEAMAUDSBGACAognEAAAUTSAGAKBoAjEAAEUTiAEAKJpADABA0QRi\nAACKJhADAFA0gRgAgKIJxAAAFE0gBgCgaAIxAABFE4gBACiaQAwAQNEEYgAAiiYQAwBQNIEYAICi\nCcQAABRNIAYAoGgCMQAARROIAQAomkAMAEDRBGIAAIomEAMAUDSBGACAognEAAAUbXijC3ijzs7O\nnHPOOXnooYfS3NychQsXZptttuma/4//+I9ZunRpJkyYkCQ599xzM2nSpEaVCwDAEDfoAvEtt9yS\ntWvX5oYbbsiKFSvyla98JZdffnnX/Pvvvz8XXHBBPvzhDzewSgAANhWDLhBXKpXsvvvuSZLJkyfn\n/vvv32D+Aw88kCuvvDLPPvts9txzzxx99NGNKBMAgE3EoAvEbW1taWlp6Xo9bNiwrF+/PsOH/1ep\nf/VXf5XDDjssLS0tOf744/OTn/wkH/vYx952vZVKpW41v9HatWs32Obbve7r7zSi1nrVMVB6Op7u\nxjcQPevJMdIfA73/6tGTvpxXvd1GX2vt7XZ6s4161tHbWmq1jtd7q98ZLOfVQF2/a/F3o1bbsY7B\ndf2uRx39+ZvX11oGm0EXiFtaWtLe3t71urOzsysMV6vV/O///b8zduzYJMkee+yRX//61z0KxK2t\nrfUpeCOam5uzdu3arm02NzdvUMMbX/dkmY39Tq1q7c123zi2oaYnfa1UKt2Ory89e+M2eltnX9ez\nMW83vnqoR096cmz2djv17Ht32+npNmq972ox3lr2rLvxDZbzqq/X795eN2vxd6NW2+nJOrr7mzeQ\nddRjHf3dd32tpb96Wkd//+b1tZZG6C6QD7pPmZgyZUqWLVuWJFmxYkW23377rnltbW3Zb7/90t7e\nnmq1mnvuucezxAAA9Mugu0P8iU98InfeeWcOOeSQVKvVLFq0KDfddFNefvnlzJ49OyeddFLmzJmT\n5ubm7Lbbbtljjz0aXTIAAEPYoAvEm222Wb70pS9tMG277bbr+vmAAw7IAQccMNBlAQCwiRp0j0wA\nAMBAEogBACiaQAwAQNEEYgAAiiYQAwBQNIEYAICiCcQAABRNIAYAoGgCMQAARROIAQAomkAMAEDR\nBGIAAIomEAMAUDSBGACAognEAAAUTSAGAKBoAjEAAEUTiAEAKJpADABA0QRiAACKJhADAFA0gRgA\ngKIJxAAAFE0gBgCgaAIxAABFE4gBACiaQAwAQNEEYgAAiiYQAwBQNIEYAICiCcQAABRteK1W9NJL\nL+Wmm27Kiy++mGq12jX9+OOPr9UmAACg5moWiE844YSMHTs273//+9PU1FSr1QIAQF3VLBA/99xz\nueaaa2q1OgAAGBA1e4Z4xx13zIMPPlir1QEAwICo2R3ihx9+OAceeGDe8Y53ZOTIkalWq2lqasqt\nt95aq00AAEDN1SwQX3rppbVaFQAADJiaBeKtt946119/fe6+++6sX78+H/3oR/OZz3ymVqsHAIC6\nqFkg/ru/+7usXLkyBx10UKrVan7wgx/kiSeeyBlnnFGrTQAAQM3VLBDfeeed+eEPf5jNNvuv9+nt\nueeemTFjRq1WDwAAdVGzT5no6OjI+vXrN3g9bNiwWq0eAADqomZ3iGfMmJE5c+bkr/7qr5Ik//Zv\n/9b1MwAADFY1C8THHHNMdtxxx9x9992pVqs55phjsueee9Zq9QAAUBf9fmTigQceSJLce++92Xzz\nzbPXXntl7733zpgxY3Lvvff2u0AAAKinft8hvv7667Nw4cJcfPHFb5rX1NSU73znO/3dBAAA1E2/\nA/HChQuTJGeddVa23377DeatWLGi1+vr7OzMOeeck4ceeijNzc1ZuHBhttlmm675t912W/7hH/4h\nw4cPz0EHHZRPf/rT/RsAAABF63cgrlQq6ezszJlnnpkvf/nLqVarSZL169fnnHPOyc0339yr9d1y\nyy1Zu3ZtbrjhhqxYsSJf+cpXcvnllydJ1q1bl/PPPz/f//73M3r06Bx66KHZa6+9suWWW/Z3GAAA\nFKrfzxDfddddufjii/PMM8/koosuysUXX5yLL744V155ZWbPnt3r9VUqley+++5JksmTJ+f+++/v\nmvfII49k4sSJ+bM/+7M0NzentbV1yDyn/MQTT2TbbbfNtttumyeeeKJPy7x+/p/+mzdvXtf8efPm\n9Xr+221nY/OfeeaZftXR11prsY6e9PWiiy6qec96u+96coz0tWczZszo1Tp6u52+9v3t9m9f+t7b\nntWq733ZTk+Oke723VvVUu/x1rJn3Z17g+W86uv1+43Xzf4e7z1dZjCcm4NpHX3Zv739m1fP63c9\n6ujv37y+HmeDzbBzzjnnnP6s4CMf+UhmzpyZ//E//kfOOOOMzJw5MzNnzswBBxyQXXbZpdfr+/GP\nf5wdd9wx2267bZLkO9/5Tj7zmc9ks802yx/+8Ic89NBD+eQnP5kk+cUvfpGRI0fmQx/6ULfrfOqp\npwb0vz/+8Y+ZNGlSJk6c2PX66aefTkdHRzo6OjJmzJh87GMf65rfk2XeOL+joyNPPfVUfv/732fP\nPffMU089ldNPPz1PPfVURo0a1aP5PdlOPeroS621WEdPxvPUU0/l6aefbnjPenKM9LVnzzzzTK96\nNhDHWU/2b0/6/vGPf7xfPatF3/uynZ4eI93tu77sm1qMt5Y96+7cGyznVS2u37U43vuynXqem68/\n9+ox3lr1rF7n5kBdv+txfvf3b15fjrNG/ZckW2+99UazYlP1T8849NEll1ySuXPn5vTTT9/o/PPP\nP79X6zv//POz8847Z999902STJ8+PcuWLUuSPPjgg1m8eHG++c1vJkkWLVqUKVOmdAXkt1KpVNLa\n2tqrOvprILb5p380/OEPf+jT675649j6st1a1F6P8W677bZZu3ZtVq1a1ed19HQ7r19vX2vtbh1v\n1bPuxlevfdXbsfR1PW933g1E3+u5nbc7Ngdi39SzZ/099wbLeDfm9cdmrY73tzOQ52Z3514txlur\nnvWlJ739m1eLOobS37x6nTP10N1x2u9niP90d3bXXXft76qSJFOmTMlPfvKT7LvvvlmxYsUGb9Tb\nbrvtsnLlyrz44ovZfPPN8/Of/zxHHXVUTbYLAECZ+h2I99prryTJgQcemGeeeSZbbbVVfv7zn+eh\nhx7KgQce2Ov1feITn8idd96ZQw45JNVqNYsWLcpNN92Ul19+ObNnz85pp52Wo446KtVqNQcddFDe\n9a539XcIAAAUrGbfVHf22Wdns802y1//9V/nlFNOyZ//+Z/n7rvvziWXXNKr9Wy22Wb50pe+tMG0\n7bbbruvnvfbaqyuEAwBAf/X7Uyb+5D//8z+zYMGC/OhHP8rBBx+cRYsW5cknn6zV6gEAoC5qFog7\nOjrS2dmZW2+9NdOnT88rr7ySV199tVarBwCAuqhZID7ggAMybdq0vPvd787OO++cmTNn9ulziAEA\nYCDV7Bniz372s5kzZ05eeeWVvPTSS/nud7+bCRMm1Gr1AABQFzULxI8//nhOOumkPP744+ns7My7\n3/3ufP3rX+/6PDoAABiMavbIxIIFC/K5z30u99xzT+6999787d/+bc4666xarR4AAOqiZoH4hRde\n2OAb4/bdd9+8+OKLtVo9AADURc0CcXNzcx544IGu1/fff39Gjx5dq9UDAEBd1OwZ4i9+8YuZO3du\nxo8fn2q1mv/3//5fvva1r9Vq9QAAUBf9DsRPP/10zjvvvKxcuTK77bZbDjzwwIwdOzbve9/70tzc\nXIsaAQCgbvr9yMQZZ5yRSZMmZd68eens7MyNN96YD3zgA8IwAABDQk3uEF911VVJkt122y0HHHBA\nv4sCAICB0u87xCNGjNjg59e/BgCAwa5mnzLxJ01NTbVeJQAA1E2/H5l4+OGHs/fee3e9fvrpp7P3\n3nunWq2mqakpt956a383AQAAddPvQHzzzTfXog4AAGiIfgfid7/73bWoAwAAGqLmzxADAMBQIhAD\nAFA0gRgAgKIJxAAAFE0gBgCgaAIxAABFE4gBACiaQAwAQNEEYgAAiiYQAwBQNIEYAICiCcQAABRN\nIAYAoGgCMQAARROIAQAomkAMAEDRBGIAAIomEAMAUDSBGACAognEAAAUTSAGAKBoAjEAAEUTiAEA\nKJpADABA0QRiAACKJhADAFA0gRgAgKIJxAAAFG14owt4vVdffTXz5s3L888/nzFjxuSCCy7IhAkT\nNlhm4cKF+cUvfpExY8YkSS677LKMHTu2EeUCALAJGFSB+Prrr8/222+fuXPn5t/+7d9y2WWX5cwz\nz9xgmQceeCDf+ta33hSUAQCgLwbVIxOVSiW77757kmT69On5j//4jw3md3Z2ZuXKlVmwYEEOOeSQ\nfP/7329EmQAAbEIadod46dKl+fa3v73BtHe84x1djz+MGTMma9as2WD+yy+/nM985jP57Gc/m46O\njsyZMycf/vCHs8MOO7zt9iqVSu2K76F6b3Pt2rUbbKe3r/vj9evoy3ZrUXs9xlvPntV6OwPVs4E4\nznqy3Z7qbvmBOkaG0v7tbx21WsdQqrWvejP+Whjoc/Ot1luL8daqZ33tifO7vusYDBoWiGfNmpVZ\ns2ZtMO34449Pe3t7kqS9vT3jxo3bYP7o0aMzZ86cjB49Okny0Y9+NA8++GCPAnFra2uNKu+ZSqVS\n9202Nzcn+e+x9fZ1X71xbH3Zbi1qr8d4m5ubs3bt2pr3bGPbef16+1prd+t4q551N7567avejqWv\n63m7824g+l7P7bzdsTkQ+6aePevvuTdYxrsxrz82a3W8v52BPDe7O/dqMd5a9awvPent37xa1DGU\n/ubV65yph+5C+qB6ZGLKlCm54447kiTLli17UzP/8Ic/5NBDD01HR0fWrVuXX/ziF/nQhz7UiFIB\nANhEDKo31R166KGZP39+Dj300IwYMSKLFy9OklxzzTWZOHFi9t577+y///759Kc/nREjRmT//ffP\n+9///gZXDQDAUDaoAvHo0aNz8cUXv2n6Zz/72a6fP/e5z+Vzn/vcQJYFAMAmbFA9MgEAAANNIAYA\noGgCMQAARROIAQAomkAMAEDRBGIAAIomEAMAUDSBGACAognEAAAUTSAGAKBoAjEAAEUTiAEAKJpA\nDABA0QRiAACKJhADAFA0gRgAgKIJxAAAFE0gBgCgaAIxAABFE4gBACiaQAwAQNEEYgAAiiYQAwBQ\nNIEYAICiCcQAABRNIAYAoGgCMQAARROIAQAomkAMAEDRBGIAAIomEAMAUDSBGACAognEAAAUTSAG\nAKBoAjEAAEUTiAEAKJpADABA0QRiAACKJhADAFA0gRgAgKIJxAAAFE0g5v9r787jqizNE0GPAAAg\nAElEQVTz/4+/OKyHRTZBQDbZFEwUcENJxCXHdaxGHbUmzUda026PnNb5NjPOlFFZllk6LumI5pYo\nqEkNUipuuKGCoCFqKaCgAiLbuX9/+OOMCyiaec5983n+5eHgOdf7XJ/7ui7u7QghhBBCtGiyIBZC\nCCGEEC2aLIiFEEIIIUSLJgtiIYQQQgjRopnlgjgtLY1XXnml0edWrFjBI488wujRo0lPT7/PLRNC\nCCGEEFpjZeoG3Gj69Ols3bqV8PDwm54rKSlhyZIlrF69murqasaNG0fv3r2xsbExQUvNw+nTpwkM\nDDT+29fX946eN0U77rat9+I1mqO4uNgsPrN78RqNfWaenp533I77UWfN6d974X587r/V+9yq75p6\nn/uV9168xr3Y9swl7528x/16H1Num+byGje+jjm/hprmvPu11vgtmd2CODo6mgEDBvD111/f9NzB\ngweJiorCxsYGGxsb/P39yc3NJTIy8ravm5WV9Vs016Tv2adPH7777jtqamoA8PT0pE+fPsb3vd3z\nv8a1r3Gn7bibtt6L12iO3/Izu9fvc7ef2YABA+7oM7sfddac/m2uW/3+/aqR3+p9btV3jb2PuWwT\npvzMzGkMuJO+uxfu97bZ1Ovei7z36jO728/kfteq1rZvNbBQFEUxxRuvXLmSr7766rqf/etf/yIy\nMpKdO3eyfPlyZs6ced3zycnJ5OXl8eqrrwIwbdo0Ro4cSa9evW75XllZWcTExNzbALdhive8X7Sc\nDSSfmmk5G0g+NdNyNtB2Pi1nA+3nu9atsppsD/GoUaMYNWrUHf0fR0dHKisrjY8rKytxcnK6100T\nQgghhBAtiFleVNeUyMhIsrKyqK6upry8nOPHjxMWFmbqZgkhhBBCCBUzu3OIG7Nw4UL8/f3p378/\njz/+OOPGjUNRFF5++WVsbW1N3TwhhBBCCKFiZrkg7tGjBz169DA+njhxovHfo0ePZvTo0aZolhBC\nCCGE0CBVnTIhhBBCCCHEvSYLYiGEEEII0aLJglgIIYQQQrRosiAWQgghhBAtmiyIhRBCCCFEiyYL\nYiGEEEII0aLJglgIIYQQQrRosiAWQgghhBAtmiyIhRBCCCFEiyYLYiGEEEII0aLJglgIIYQQQrRo\nsiAWQgghhBAtmiyIhRBCCCFEiyYLYiGEEEII0aLJglgIIYQQQrRoFoqiKKZuxG8tKyvL1E0QQggh\nhBAmFhMT0+jPW8SCWAghhBBCiKbIKRNCCCGEEKJFkwWxEEIIIYRo0WRBLIQQQgghWjRZEAshhBBC\niBZNFsRCCCGEEKJFkwWxEEIIIYRo0WRBLIQQQgghWjTLd9555x1TN0LcmZqaGtasWUNVVRUeHh5Y\nWlqaukn3lJbzaTkbaDuflrOB5FMzLWcDyadmasomC2KVOXbsGJMnT8bGxoYDBw5QWFhIYGAg9vb2\npm7aPaHlfFrOBtrOp+VsIPnUTMvZQPKpmdqyyYJYZXJzc3FxceHll1/Gz8+Po0ePcuTIEbp162bq\npt0TWs6n5Wyg7XxazgaST820nA0kn5qpLZucQ2zmioqK+Nvf/saGDRv45ZdfKC8vZ9euXQAEBQXR\nq1cvTp06xbFjx0zc0ruj5XxazgbazqflbCD51JxPy9lA8qk5n9qzyYLYjOXn5/OXv/wFHx8fKisr\neeGFFxg0aBBnzpwhIyMDa2trfHx8cHFx4fz586Zu7h3Tcj4tZwNt59NyNpB8as6n5Wwg+dScTwvZ\nZEFshgwGAwD19fW0bt2ap556ilGjRuHp6cmiRYt48803+fDDDwHw9vamqKgIvV5vyibfES3n03I2\n0HY+LWcDyafmfFrOBpJPzfm0lE0WxGZIp7vaLZWVlXh4eJCfnw/A//3f/7Fw4UI6d+5Mly5dmD59\nOk8++SQAbdq0MVl775SW82k5G2g7n5azgeRTcz4tZwPJp+Z8WsomF9WZmKIo1NTUsHXrVhwcHHBw\ncKCqqork5GQ6dOhAZmYmDg4OeHp64u7uzpkzZ/jll1949tlnCQwMxM/Pj6effhpHR0dTR2mU1vOV\nlZWRmJhIZWUlrVq1wsHBgdWrV2sim/SderMBXLhwgc8//5zLly+j0+lwcXFhxYoVhIeHqz6f1KZ6\ns2m977SeT8u1KXuITczCwoKDBw8yf/58Dh06BIBer8fKygpfX19iY2PZv38/27dvB64enggLC8PK\nyorAwED69etnyubflpbzZWdn8+yzz+Lv74+dnR21tbVYWFhgbW2t+mwgfafWbAA7duxg0qRJGAwG\njh49yoIFC4Cr/aeFfFKb6swG2u470HY+rdem7CE2IUVRqK2tZfbs2RQXF+Pg4ICrqyvu7u506NAB\ngNDQUKqqqkhPTycpKQlFUXj00Uexs7Mzceubp6amRrP5cnNz8fHxoUePHixbtgydTkddXR3x8fGA\nurPV19dTV1cnfafCbAAbNmxg4MCBPP7449ja2nLs2DH69OlD+/btAXXn0/q4qeXa1HrfaT2flmsT\nZEF83+Xn5/PJJ5+gKArW1ta4ubnh4ODAgAED+Omnn6iuriYsLMx4Xs6FCxfo2LEjXbt2pWvXrowb\nN86si+vYsWN88MEHVFdXY2tri5ubG46OjprJ98orr2Bvb09AQABpaWns3r2b6upqevbsSVFREWvX\nrqVTp064uLhQWlrKAw88oIpsiqJQX19PYmIiHTp0wNHREQsLC1xcXOjXr5+q++7GbPb29mzatIm9\ne/dqou/g+u2uTZs2FBYW4uvri4+PDydPnmTTpk2MGDHC2HdlZWWqyqfVcbMl1KZW+66B1vNpdc5r\njJwycR/t2bOHv//977Rv356CggJef/11ADp37kznzp1p164dBQUFHDlyBICLFy8yY8YMzp07h6ur\nKyEhIaZs/m3t2rWLd955h44dO1JYWMisWbMA6NSpk+rz1dXVAXDkyBFSU1OpqalhzJgx7N69m4qK\nChISEvjjH/9IaGgo+/bto6KigsTERFVkg6uH+c6fP8/333/P8uXLgauTdUREhOr77tpsSUlJAIwZ\nM4Zdu3Zpou+u3e4KCgp49913GTt2LDExMQCkpaUxcOBALC0tqauro6qqSjV9B9oeN7Vem1ruO9B2\nPq3PeY2RPcT3gcFgwMLCgoKCAsrKynjhhReIjo5m5cqVVFdXGycuT09PDh06xNmzZwkODsbFxYW+\nffua7QnoDerr69HpdBw6dMh4/8H6+npKS0uJiYkxfk2jWvPB1StpKyoqKCgo4MyZM7i4uBAeHk5N\nTQ2ZmZk8+uijWFtb880339C3b1/8/PxUkw2untqyePFi3N3dOXjwIEFBQfj4+GBpaYlOp1N1312b\nLTs7m4CAAIKDg6moqGD37t2q7bvGtjuDwcC5c+eIiYnBysqKyspKtm7dyqRJk0hJSWHFihXExcXx\n0EMPqSafVsdN0H5tarXvtD6ng/bnvMbIgvg3piiK8VDJ8ePHuXDhAr6+vjg7OxMWFsa7777LuHHj\nsLS0RK/XU1lZadwzZ21tbfy/5urafIqi0LNnTywtLZk6dSrl5eWkpqbSp08f9Hq96vIpioKFhYXx\n8ZYtWwgKCmLQoEG8++677Nixg5deeomdO3eSmZnJwoULcXBwYOjQodjZ2WFpaWnC1t/etfksLS2p\nra1l5MiRxkFuyJAh6HQ6DAYD9vb2qu27xrINHTqU2NhY0tPT2bFjB/Pnz8fR0VFVfXer7S4lJYX4\n+HguXbrE+++/z86dOzl//jxPP/00rVu3Nuu+a6DlcbOBFmsTtN13Wp7TtT7n3Y4siH8Dp0+fZvXq\n1bi6umJvb09NTQ2pqamEhoaybds2PDw88PDwwNfXl4MHD1JdXW084T4oKIiOHTtibW1t4hRNO336\nNOvWrcPV1RW9Xk9tbS0rV66kd+/euLi4YGNjQ0REBFOmTOG///0vp06dokePHoD552vI1pBDURSS\nkpLo3LkzxcXF7N69m+zsbHJzc/H19WX48OHEx8cTEhJCp06dmDhxInq9/rpBxZw0lm/ZsmV06tQJ\nPz8/9Ho9/v7+fPvtt+h0OkJDQ6mrq8PS0lI1fddQl3V1dSxfvvymbJs3b6a+vp4OHTrQu3dvwsLC\niIyMVE3fNXe7azh0uXnzZt566y0mTJiAi4uLqWM06dSpU8yYMQNra2ucnJwwGAxs2LBBE+PmpUuX\n2L59Ox4eHtjY2HDhwgXWrFnDAw88oIna1HLfwc3jpsFgYP369ZrIp/U5706Y758qKrV+/Xr+/Oc/\n8/PPP/PZZ5+xf/9+7OzssLGxISQkhI4dO7Jz506ysrIAsLGxITIy0sStbr4NGzbw7LPP8vPPPzNv\n3jySk5Oxs7PD3d0dKysr4OrhMh8fH+DqN9M88MADpmxys61bt45nnnmGkpISli1bxqpVq7CxsTHe\nRDw3N5eMjAyio6P597//zfHjx8nLy8Pe3p7g4GC6d+9u4gS31lQ+T09PLCwsjHsunJycGDNmDJ9/\n/jl1dXVmO5Bfq7G6tLW1bTLb3Llzqaurw9nZWRV9d6fbnY+PD8HBwXTp0oUtW7bQtWtXUzb/tjIy\nMnjttdfo3LkztbW1WFpaYmtrq5lxMyMjg//85z/k5OQA4OjoiIuLiyZqU+t9l5ycfNO4aWtri729\nverzaX3Ou1Oyh/geS0tLY8SIETzxxBPs2rULvV5PREQEoaGhAISFhXHp0iVSU1NJSkrC3d2doUOH\nGic1c7dt2zZGjBjB2LFjcXR05IcffsBgMDBw4EDg6iGXDRs2kJSUxOLFi3FycmLs2LFmv6iqr69n\n06ZNjB8/nj/84Q+4uLiwYcMGHB0dSUhIAKBDhw488sgjdOzYEVdXV1xdXYmMjFRF3zUn37X8/f2N\nA76lpaXZ//V/u7q8ltqygXa3uwZ79+6la9eu+Pr68s033xgPvcbGxgLqHjfLy8v5+OOPqampwcbG\nBm9vb5ydnY1zwrXUWJv79+8nJiZGk30HsHHjxkbHzYZ76qo1n9bnvLshC+Jfac+ePXzxxRdYWlri\n7e1NVlYWRUVFnD59mrS0NCoqKown1NvY2FBWVkZUVBSdO3cmISGBkSNHmm1xKYpCZWUlM2bMIC4u\nDp1Ox5IlSzAYDMTExODs7IzBYGDbtm3ExsZiZWXFhQsX6Ny5M0FBQSQkJPDII4+Y5aR8YzZLS0vm\nzZuHtbU1MTEx6PV6MjIyOHv2LD179sTKyorz58/j5uZm3AsSHBysmr5rTr6ioiLjBRE6nY7w8HCs\nrKzMblK+m7pUSzbQ9nbX4Npxs02bNqSnp7N//35qamp48MEHOXbsGOvWrSM+Pt54ikGXLl1UMW5m\nZWWRmJhIYWEhPj4+tG7dGg8PD3r16kV+fj6Kolx3BX7D/WpBnbW5atUq8vPzuXLliur7Dq6vTR8f\nH1atWkVNTQ3dunVrdNy8ePGiKvJpfc67F+SUiV9h8+bNzJo1i6ioKDIzM/nrX//Kc889R2BgILNn\nz2bChAlMmjSJI0eOkJaWRlFREZ9++imXL1/G29sbPz8/U0e4JQsLC+PCvuFWXE8++SRLly7l8uXL\nODo6EhAQgK2tLefPn+fUqVPMnDmTy5cvExwcTFBQkIkTNK2xbFOnTiUpKYm1a9fy/vvv4+Pjg729\nPeXl5Zw6dcr4NbjmvNBocDf5Zs+ezeXLl03c8tu7m7pUSzbQ9nYHN4+b//jHP/jTn/7Et99+i6ur\nKw8++CCPPfYYnp6e7N+/n19++YVZs2apYtxct24ds2bN4pFHHqG8vJxXX30VgKioKLp164aXlxd5\neXkcP34cgJMnT/LZZ5+psja//vprAEaNGsXGjRtV33fQeG2+8cYbLF++vMlx85NPPlFFPq3PefeC\n7CG+Cw1XYmZnZ2Nvb8+ECROIi4vjgw8+IDAwEEdHR3766SemTZtGq1atyMzMpF+/fgQEBJCQkKCa\n4qqoqGDBggUEBwezdetWevToQUhICLm5ucZMtra2JCcnM2zYMDw8PFSTr7FsoaGhdOrUiZKSEoKD\ngxk+fDgZGRn0798fd3d31WQDbefTcl2CdvM1NW6+//77xMXFYWdnx6FDhxg8eDBw9dzN4cOH4+Pj\nY/b5Gm4zlpmZiZ+fHw8//DCRkZFkZmby4IMPGvcAu7q6cuDAAcrKyggPD8fNzc3ss13r2tr88ccf\n6dGjB0FBQeTn53P69GkGDRoEqKvv4H+3UbuxNt977z1iY2N57LHHyM/PJzQ0VFPjplbmhHtFFsR3\nyGAwGC+CyM3Npbq6Gj8/PxwdHXFzc2PevHk8//zzxj3Bs2fPxt3dnd/97ndYW1ub5WGwBpcuXcLW\n1tb42MbGhtraWsaPH09JSQlbtmyhf//+xMbG8vXXX3P06FHmzp1Lp06diI2NxcLCwqzzXevGbBkZ\nGfTr1w8vLy/jhS+JiYnGbxTS6XRmne12fafmfD/99BOurq7GBZUW67JhQgZtbnfX3s6psXFz1qxZ\nfPTRR6xfv954Oyd/f38GDBhgtqcPwP9qs2FOqKqqonv37jg5OfHdd99RUlLC7373O+PvOzs7U1JS\ngoODAyEhIWa93TW4VW2mp6fTv39/evfuTUpKCjt37mTBggWq6Dv437jZVG26u7uTmJjIlClTjKe7\naGXcVPOc8JtRxG3l5OQo8+bNU37++WelpqbG+PN9+/Ypb775prJ7926ltrZWURRFeeKJJ5SjR48q\nZ86cUdLT05WsrCxTNbvZjhw5okydOlVJTU1V6urqrnuu4fGZM2eUxx57TMnMzFQURVFKSkqUrKws\nZf/+/fe9vXciJydHmT59upKVlaWUlpYqiqIY++rGbDt37lQURVF++uknZfXq1cbH5uxO+k5t+XJy\ncpQXXnhBefHFF5W6ujrFYDAoBoNBURT116WiXM23YMEC5ezZs8Y89fX1iqJoI19ubq6yZMkSpbi4\n+LrabGzcnDBhgpKdna1UVVUp2dnZSnZ2tqma3Sw31uaN256iKMrzzz+vpKWlKYqiKOfOnTP+vLHf\nNTeNzXm3q83S0lJV9J2i3DxuNmRrrDaffPJJ5eDBg8qZM2eUVatWaWrcVNuc8FuTPcS3sWjRIr78\n8kucnJzYvn073t7eeHp6AhjPBysoKMDe3h43NzcyMzMZNGgQHh4eBAYG4u3tbeIETVMUhY8++ohF\nixbx7LPPEh8ff9NNwxseOzo6Ultby8KFCxk1ahT29vZ4e3vj5eVliqY3y8KFC1m0aBFhYWHk5OSQ\nl5dHt27djJluzLZgwQJGjRqFq6sr4eHhtG3b1pTNv6W76Tu15KuoqGDmzJls2rSJK1eu0LZtW/r2\n7XvdnlA11yXA/PnzmTt3Lg4ODvz44494enri5eWlmXwN46aDgwM7d+7k4sWLxvuyNjZu7tixgyFD\nhuDk5ISnp6dxjDU3TdXmtduewWCgrq6OXbt2MXjwYObMmcPq1at56KGHjN/8aM6amvOaqs1Fixbx\nhz/8Ab1eb9Z9B02Pmw3ZGqvN7du3M3ToUDw8PIiIiNDUuKmWOeF+0e7lgveAoiicOHGC9957j+Dg\nYJ577jkqKiqu+51Ro0aRnp7OvHnzuHjxIlFRUbi6upqoxXemtrYWZ2dnxo4dS1lZGa+99ho9evTg\ngQceIDQ09LrTQwCGDx9uvHE3YLaHU5T/f4goLy+PDz/8EC8vL5KTkykrK7vu+WupJVsDRVGMt9bS\nUt/B1av0nZyc+OKLLzh06JDx/p6N9RuoKxtc/bre4uJi/va3v9GxY0deeeUVqqqqjM+rue8aXLhw\ngXfffZeQkBC2bt1Kamoqvr6+xvshq3XcbE5t6nQ6Tp06xYoVK8jLy6Nfv358+umnqjgX83Zzntpr\n83bjJmi7Nq+ltr67H2QP8Q2+//57kpKSsLKywt/fnxMnThAVFcUvv/zChx9+iJ2dHUeOHDEO7Pb2\n9kRERNCxY0eGDx/OgAEDzLqwiouLmTlzJn369MHS0pL8/HzS0tI4f/48gwcPJj8/n5SUFIYOHXpT\nDmtra8LDw832nMXvv/+e2bNnc/LkSaKjo8nOziYyMhJHR0e+//57Ll26RK9evRptu7lng+trs23b\ntpw5c4aUlBRKS0tV33fFxcV89NFHxMfHExgYSLdu3YCrV+0fP36c/v37A40P2uaeDW7e7jZs2MCV\nK1fIycnhv//9LzU1NZw6dYrIyEjV9R1crc2vvvoKg8FAUFAQCxcuxM3Njfbt2+Pk5ERlZSX79u0j\nLi4OUNe4eTe1eeDAAVxcXHj99dfp27evWX+l7Z3MeWqtzeaOm6D92myghr6732RBfI1ly5aRnJxM\n//792bx5M1lZWTz33HPG7yPv1asXMTExbNy4ERcXl+tuseLi4mK8kticZWVl8eWXX+Lv709QUBC2\ntraUlZXx1FNPER4eTocOHdi1axdeXl5mfejrRosWLWLjxo1MmjSJzZs3s337dt544w0cHR25fPky\nc+fO5c9//jOurq5UVVVhbW3d5F/O5uja2kxLSyM7O5vRo0dTUFDAlClTVN13cLUu586dS0BAAEFB\nQdTU1GBpaYmzszN79uyhW7du2NnZmbqZd61hu/P19SU4OJjw8HBOnz7NggULeOONN+jcuTMpKSk4\nODgQEBBg6ubekaVLl7Ju3ToGDhzId999x5UrV4iLiyMxMZHHHnsMvV5PRUUFhYWFREREoNfrjf9X\nDePmndRmw5ji7+9PfHw8Tk5OJm79rf2aOU8Nfs24qbXaFLdn3icz3SfJycls374dOzs7EhISGDJk\nCG+99RabNm0iNzcXgNatWxMXF0fr1q1p1aoVERERJm51861fv560tDQURSElJYXBgwezYMECrly5\nQmhoKBMnTsTe3h7AeHgsLCzMlE1utrVr17J161YABg8eTFRUFM8//zwGg4Ha2loA4y1l2rVrx6xZ\ns1iwYAF1dXWqWAw3VptvvPEG69ato7i4mFdffdV4dwm19V1jdTl//nyqq6uxsbEBrl4F7u7ubuKW\n3p2UlJSb8i1atIjq6mratm1rPGIRHx9P27ZtcXJyUtW4kpyczLZt26ivr2fgwIEMGTKEiIgIamtr\n6dmzJ+3ateOzzz4DwM3NjeLiYtzc3Ezc6ua529q88VxNc7V27VpNz3kybqp33DQl2UMMXLlyhZSU\nFPR6vfGwirOzM7W1tSxfvpyRI0cydepU8vLyWLhwIaGhocTFxanmUENVVRXp6enEx8djYWHBhAkT\nOHDgALm5ufTo0YOqqiqmTJlCXl4eS5cupUuXLsZDL+aer7q6mvXr19OtWzd69uyJnZ0dq1evxtbW\nll69egGQmZnJ/Pnz2bZtG+7u7jz99NPGgcPcVVVVkZqael1turi4UFNTQ1JSEr///e954oknOHr0\nKElJSarqu8rKSrZs2dJkXQJ4eHjw4Ycf0r59e9XtnaqsrGx0uzty5Ag9e/bEwcGBL774gsrKSubO\nnUtwcHCjF0eaq4Zxs1+/fsZvYXvzzTfR6XT88MMPvP766yxevJjDhw+TlJREr1696NKlC2D+tXm7\nMRPUWZsNtxlrbFzR2pwn46a6atMctMgF8cmTJzl48KDx0GTDfRbt7Ow4fPgw/v7+eHp6EhMTw/Ll\ny4mPj6d79+7odDpGjhzJkCFDzPoefTfmAygrK6N9+/a0a9fO+DXT//nPf4iJicHb25uoqChcXFwY\nPnw4Dz30kNkOfI31XXV1NX369KFVq1bU19cza9Ysxo8fT5s2baipqeHHH3/k4sWL/POf/2Tw4MFm\n/dWTe/bsYcmSJTg7O+Ps7IyDgwO1tbXo9fqbanPZsmUkJCSQkJCAk5OT2ffdjdmcnJwoKiqiQ4cO\nN9Vl9+7dcXZ2RqfT4e3tTbt27WjVqpWpI9xSVlYWaWlp+Pv7Y2tri42NDefOnbtpu1u6dCnR0dGE\nhYXRu3dvAB5++GGGDRtm1ovhG/NZWVlRXl5ObGws9vb22NraMmzYMMaMGcP06dPp06cP48ePx87O\njt///vf079/fbGvz7NmzfPDBB/Tt2xe4eurDhQsXGh0z1Vibu3bt4oMPPsDNzQ1/f38URUGn02Fr\na6uJOS8zM5M5c+bQpk0bXF1dsbKyQlGURud0tY2bN2aztbWltLRUM7VpTsx39P0NLVmyhNTUVE6c\nOGH82f79+xk2bBhubm5s2bKFgoICioqKCAgIwNnZGX9/fwYOHGi8EtWc3ZhPURTy8vLQ6XRYW1tj\nMBjo0KEDsbGxvP/++wDGvVPmnu/GbBYWFhw+fNh4+KusrIyQkBDc3d157bXXmDFjBhMnTmTp0qVm\n/5fyjBkzmDNnDq6urqxZs4aUlBQsLS05cOBAk7Xp5OSEn5+f2fddY9l0Oh35+fmN1uWMGTOM/7fh\nlAJzlpiYyMcff8zJkyf59NNP+fnnn1EUpcl8iYmJAISEhDBo0CCz7jtoPB9c/SKDhqMtBoMBb29v\nampqiImJwcHBAQcHB3r37k1ISIgpm39bhw4dYuPGjXzzzTfA1SxNjZlqqs3a2lqmTZvG559/zpQp\nU3jwwQexsLBAp9Oxd+9eTcx5X375JYsXLyYuLo7i4mJjf+3bt0/142Zj2err6zl69Kjqa9MctagF\nsaIoHD9+nO3bt1NVVcWuXbu4cuUKPj4+6PV6vv32W5544gmcnJyYMWMGL7/8Mh06dFDN4fWm8nl7\ne+Pi4sKaNWuA/53fNn78eIYNG2bKJjfb7bKtXr0awHgI7C9/+QudOnXi7bffNus9wg0qKyspKSlh\nzpw5TJ48mXbt2mFjY4Ozs7Pqa7OpbK1atWqyLocMGWLKJt+R6upqzp8/z5w5c3j77bc5efIk1dXV\neHh44OnpycqVKwF1bnfQ/Hxr165l6tSpTJo0ifDwcNq1a2filjdfdXU1o0eP5uuvv6a2tpa2bdvi\n4eHRaN+pqTYVRcHLy4uxY8eSm5trPI/W2dkZb29vUlNTVTuuNKiurubFF180ns18wpMAAAZRSURB\nVKK0ZcsW3N3d8fLyUn2+xrK5ubkREBDAihUrAPXWpjnS/CkTa9asITU1FQsLC/z8/NDpdHTu3Bl/\nf3/y8vLQ6/W0bduW1q1bk5eXR9euXYmKiiI0NJRx48YZz8kxV3eS78SJE3Ts2NG4Adnb25v1hQR3\nkq2wsJDIyEj27t1Lu3btePvtt43nhJmrhnxWVlYEBgZy4MABoqOjsbGxYeXKlTg5OREZGYmnp6fq\narO52RrqMiIiwnhrKnOvS7g+X0BAAIWFhXTp0oU9e/bw5ZdfYmtrS0FBAYMHD+bYsWOazxcZGUlU\nVBTe3t48/vjjxMbGmjpCk24cV+Dqnrjnn3+esrIyFi5ciE6nIy4ujoKCAlWNmfC/fDqdjoCAAI4e\nPUpKSgoWFhYkJCSQlZVFRkYGY8eONd5OTS3jClyfz9fXl/nz53PhwgUAunXrxtatW8nMzGTUqFHk\n5OSoKt+dZDt+/LjqxhVzZ/67zu6SoijMnj2bvLw8RowYwVdffUVubi4TJ04kOjqaqqoqcnJyOHz4\nMOHh4Vy6dInLly8bi6t9+/YmTnBrd5rv4sWLVFVVqWJv6d1kq6iowMLCgqFDh5r9DfBvzDd//nzy\n8vKYNm0aAOfPn+fo0aO89dZbAJw4cQILCwtV1ObdZjP3PmvQWL6cnBwmTZoEgLOzMwsXLgSuflPi\nlStXADSd79rbF0ZHR5us7bdzY7bFixeTnZ3N5MmT8fT05MyZMxw+fJjDhw8zZswY6urqKC8vV8WY\nCTfnW7RoEYWFhQwaNIiysjKefvpp9Ho9YWFhfPzxx2RkZFBdXa2KcQVuzrdgwQKKiooYP348L774\nIqtWrSIkJIR27doxe/ZsfvjhB9Xku9NsDX2nlnFFLdSxpd8FCwsLKisrjRdzBAYG8tRTT/Hwww8b\n7y/YvXt3vvvuO/bs2UNsbKxZ3zz9RlrO92uyqWGAaCrfsGHDcHFx4cSJE8TGxmJlZcXMmTOxtbWl\nZ8+epm52s2g5GzSdb8SIEbi6uhIQEIC9vT25ubn4+Pjw6KOPUlBQYOpmN5uW8zWWbfLkyYwePZp9\n+/axd+9eJk+eTHx8PGvXruWTTz4x3qlGDW7MFxAQwJQpUxg6dCgvvfQS5eXl6PV6ioqKcHZ2ZuTI\nkcZbrKnBjfn8/f155pln2LhxI0FBQezYsYOQkBAuXryIjY0NI0aMIC8vz9TNbhYtZ1MTzZ4yYTAY\nOHjwIHZ2dvj6+uLl5UVhYSEZGRkMGDAAAF9fX/Lz82nbti0BAQF4e3ubuNXNp+V8Ws4GTefbsmUL\nAwYMYNOmTSxfvpwffviBoKAgnnnmGdXk03I2uHVt9uvXj5deeokjR46watUqIiMjiY2NlXxmorFs\nBQUFpKWlMX36dP70pz8Z71cOEBERoZpscHM+b29vCgoK2LJlC3369DHeZiw5OZnu3bsbT3FRi8by\nHTt2jIMHDzJ16lSWLl3Kli1bSE1NpWvXrnTv3l01+bScTVUUDduzZ4/y3nvvKfn5+YqiKEp5ebky\nZswYpaSkxPg7tbW1pmrer6blfFrOpihN57t8+bIybdo05cUXX1TOnTtn4lbeHS1nU5Sb8126dEkZ\nM2aMUl1drRw/flxJSUlRzpw5Y+JW3j0t52usNv/4xz8qpaWliqIoSl1dnSmb96vdqu8KCwuVzZs3\nq7bvFKXpfBUVFUp5ebny448/KmfPnjVxK++OlrOphabvMhEdHY1OpyM9PZ3S0lJOnDhBeHg4rVu3\nNv6OWs4Pa4yW82k5GzSeLywsDL1ez9///nc+/vhj1X7TkJazwc35CgsLad++PTY2NgQFBTF06FC8\nvLxM3cy7puV8jdVm+/btcXV1BVDNaWVNuVXfNdxGTa19B03nc3BwwNHRkbi4ONq0aWPqZt4VLWdT\nCwtFURRTN+K3VFpayqpVq8jKyqK8vJzRo0czcuRIUzfrntFyPi1ng6bzKddcpKRWWs4GLbc2tUDL\n2UDyqZmWs6mB5hfEDQ4fPkxYWJgqLrq6G1rOp+VsoO18Ws4Gkk/NtJwNJJ+aaTmbOWsxC2IhhBBC\nCCEao+lziIUQQgghhLgdWRALIYQQQogWTRbEQgghhBCiRZMFsRBCCCGEaNFkQSyEEEIIIVo0WRAL\nIYQQQogWTRbEQgghhBCiRft/Q/BzNFASzv0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26047df75f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 显示逐日回测结果\n",
    "engine.showDailyResult()\n",
    "# 显示逐笔回测结果\n",
    "engine.showBacktestingResult()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 吊灯止损\n",
    "使用字典保存最高最低点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "\n",
    "from vnpy.trader.vtConstant import EMPTY_STRING, EMPTY_FLOAT\n",
    "from vnpy.trader.app.ctaStrategy.ctaTemplate import (CtaTemplate, \n",
    "                                                     BarGenerator,\n",
    "                                                     ArrayManager)\n",
    "import talib as ta\n",
    "\n",
    "########################################################################\n",
    "# 策略继承CtaTemplate\n",
    "class TrailingMaATRStrategy(CtaTemplate):\n",
    "    \"\"\"双指数均线策略Demo\"\"\"\n",
    "    className = 'TrailingMaATRStrategy'\n",
    "    author = u'ChannelCMT'\n",
    "    \n",
    "    # 策略交易标的的列表\n",
    "    symbolList = []         # 初始化为空\n",
    "    posDict = {}  # 初始化仓位字典\n",
    "    \n",
    "    # 多空仓位\n",
    "    Longpos = EMPTY_STRING        # 多头品种仓位\n",
    "    Shortpos = EMPTY_STRING       # 空头品种仓位\n",
    "    \n",
    "    # 策略参数\n",
    "    fastWindow = 40     # 快速均线参数\n",
    "    slowWindow = 70     # 慢速均线参数\n",
    "    initDays = 1       # 初始化数据所用的天数\n",
    "    trailingPercent = 6.8\n",
    "    \n",
    "    # 策略变量\n",
    "    fastMa0 = EMPTY_FLOAT    # 当前最新的快速EMA\n",
    "    fastMa1 = EMPTY_FLOAT    # 上一根的快速EMA\n",
    "    slowMa0 = EMPTY_FLOAT    # 当前最新的慢速EMA\n",
    "    slowMa1 = EMPTY_FLOAT    # 上一根的慢速EMA\n",
    "    maTrend = EMPTY_FLOAT    # 均线趋势，多头1，空头-1\n",
    "    atrValue = EMPTY_FLOAT\n",
    "    longStop = EMPTY_FLOAT\n",
    "    shortStop = EMPTY_FLOAT\n",
    "    \n",
    "    # 参数列表，保存了参数的名称\n",
    "    paramList = ['name',\n",
    "                 'className',\n",
    "                 'author',\n",
    "                 'vtSymbol',\n",
    "                 'symbolList',\n",
    "                 'fastWindow',\n",
    "                 'slowWindow',\n",
    "                 'atrPeriod',\n",
    "                'trailingPercent']  \n",
    "    \n",
    "    # 变量列表，保存了变量的名称\n",
    "    varList = ['inited',\n",
    "               'trading',\n",
    "               'posDict',\n",
    "               'fastMa0',\n",
    "               'fastMa1',\n",
    "               'slowMa0',\n",
    "               'slowMa1',\n",
    "               'maTrend',\n",
    "               'atrValue',\n",
    "               'longStop',\n",
    "               'shortStop']  \n",
    "    \n",
    "    # 同步列表，保存了需要保存到数据库的变量名称\n",
    "    syncList = ['posDict']\n",
    "\n",
    "    #----------------------------------------------------------------------\n",
    "    def __init__(self, ctaEngine, setting):\n",
    "        \n",
    "        # 首先找到策略的父类（就是类CtaTemplate），然后把DoubleMaStrategy的对象转换为类CtaTemplate的对象\n",
    "        super(TrailingMaATRStrategy, self).__init__(ctaEngine, setting)\n",
    "        \n",
    "        # 生成仓位记录的字典\n",
    "        symbol = self.symbolList[0]\n",
    "        self.Longpos = symbol.replace('.','_')+\"_LONG\"\n",
    "        self.Shortpos = symbol.replace('.','_')+\"_SHORT\"\n",
    "        \n",
    "        self.bg60 = BarGenerator(self.onBar, 60, self.on60MinBar)\n",
    "        self.bg60Dict = {\n",
    "            sym: self.bg60\n",
    "            for sym in self.symbolList\n",
    "        }\n",
    "        \n",
    "        \n",
    "        self.bg15 = BarGenerator(self.onBar, 15, self.on15MinBar)\n",
    "        self.bg15Dict = {\n",
    "            sym: self.bg15\n",
    "            for sym in self.symbolList\n",
    "        }\n",
    "        \n",
    "        # 生成Bar数组\n",
    "        self.am60Dict = {\n",
    "            sym: ArrayManager(size=self.slowWindow+10)\n",
    "            for sym in self.symbolList\n",
    "        }\n",
    "      \n",
    "        self.am15Dict = {\n",
    "            sym: ArrayManager(size=self.slowWindow+10)\n",
    "            for sym in self.symbolList\n",
    "        }\n",
    "        \n",
    "        self.amDict = {\n",
    "            sym: ArrayManager(size=self.slowWindow+10)\n",
    "            for sym in self.symbolList\n",
    "        }\n",
    "        \n",
    "        self.intraTradeHighDict = {}\n",
    "        self.intraTradeLowDict = {}\n",
    "\n",
    "    #----------------------------------------------------------------------\n",
    "    def onInit(self):\n",
    "        \"\"\"初始化策略（必须由用户继承实现）\"\"\"\n",
    "        self.writeCtaLog(u'双EMA演示策略初始化')\n",
    "        # 初始化仓位字典\n",
    "        self.ctaEngine.initPosition(self)\n",
    "\n",
    "        # 初始化历史数据天数\n",
    "        initData = self.loadBar(self.initDays)\n",
    "        for bar in initData:\n",
    "            self.onBar(bar)\n",
    "        \n",
    "        self.putEvent()\n",
    "\n",
    "        \n",
    "    #自定义算法----------------------------------------------------------------------\n",
    "    \n",
    "    #----------------------------------------------------------------------\n",
    "    def onStart(self):\n",
    "        \"\"\"启动策略（必须由用户继承实现）\"\"\"\n",
    "        self.writeCtaLog(u'双EMA演示策略启动')\n",
    "        self.putEvent()\n",
    "    \n",
    "    #----------------------------------------------------------------------\n",
    "    def onStop(self):\n",
    "        \"\"\"停止策略（必须由用户继承实现）\"\"\"\n",
    "        self.writeCtaLog(u'双EMA演示策略停止')\n",
    "        self.putEvent()\n",
    "        \n",
    "    #----------------------------------------------------------------------\n",
    "    def onTick(self, tick):\n",
    "        \"\"\"收到行情TICK推送（必须由用户继承实现）\"\"\"\n",
    "        pass\n",
    "    \n",
    "    #----------------------------------------------------------------------\n",
    "    def onBar(self, bar):\n",
    "        self.cancelAll()\n",
    "        \"\"\"收到Bar推送（必须由用户继承实现）\"\"\"\n",
    "        symbol = bar.vtSymbol\n",
    "        \n",
    "        # 基于60分钟判断趋势过滤，因此先更新\n",
    "        bg60 = self.bg60Dict[symbol]\n",
    "        bg60.updateBar(bar)\n",
    "        \n",
    "        # 基于15分钟判断\n",
    "        bg15 = self.bg15Dict[symbol]\n",
    "        bg15.updateBar(bar)\n",
    "        \n",
    "        \n",
    "        # 洗价器\n",
    "        if self.posDict[self.Longpos] == 0 and self.posDict[self.Shortpos] == 0:\n",
    "            self.intraTradeHighDict[symbol] = 0\n",
    "            self.intraTradeLowDict[symbol] = 999999\n",
    "\n",
    "        # 持有多头仓位\n",
    "        elif self.posDict[self.Longpos] >0:\n",
    "            self.intraTradeHighDict[symbol] = max(self.intraTradeHighDict[symbol], bar.high)\n",
    "            self.intraTradeLowDict[symbol] = bar.low\n",
    "            self.longStop = self.intraTradeHighDict[symbol]*(1-self.trailingPercent/100)\n",
    "            if bar.close<=self.longStop:\n",
    "                self.cancelAll()\n",
    "                self.sell(symbol, bar.close*0.9, self.posDict[self.Longpos])\n",
    "\n",
    "#         # 持有空头仓位\n",
    "        elif self.posDict[self.Shortpos] >0:\n",
    "            self.intraTradeLowDict[symbol] = min(self.intraTradeLowDict[symbol], bar.low)\n",
    "            self.intraTradeHighDict[symbol] = bar.high\n",
    "            self.shortStop = self.intraTradeLowDict[symbol]*(1+self.trailingPercent/100)\n",
    "            if bar.close>=self.shortStop:\n",
    "                self.cancelAll()\n",
    "                self.cover(symbol, bar.close*1.1, self.posDict[self.Shortpos])\n",
    "\n",
    "    #----------------------------------------------------------------------\n",
    "    def on60MinBar(self, bar):\n",
    "        \"\"\"60分钟K线推送\"\"\"\n",
    "        symbol = bar.vtSymbol\n",
    "        am60 = self.am60Dict[symbol]\n",
    "        am60.updateBar(bar)\n",
    "        \n",
    "        if not am60.inited:\n",
    "            return\n",
    "        \n",
    "        # 计算均线并判断趋势\n",
    "        fastMa = ta.MA(am60.close, self.fastWindow)\n",
    "        slowMa = ta.MA(am60.close, self.slowWindow)\n",
    "        \n",
    "        if fastMa[-1] > slowMa[-1]:\n",
    "            self.maTrend = 1\n",
    "        else:\n",
    "            self.maTrend = -1\n",
    "    \n",
    "    #----------------------------------------------------------------------\n",
    "    def on15MinBar(self, bar):\n",
    "        \"\"\"收到Bar推送（必须由用户继承实现）\"\"\"\n",
    "        self.cancelAll()\n",
    "        symbol = bar.vtSymbol\n",
    "        am15 = self.am15Dict[symbol]\n",
    "        am15.updateBar(bar)\n",
    "        \n",
    "        if not am15.inited:\n",
    "            return\n",
    "        \n",
    "        fastMa = ta.EMA(am15.close, self.fastWindow)\n",
    "       \n",
    "        self.fastMa0 = fastMa[-1]\n",
    "        self.fastMa1 = fastMa[-2]\n",
    "        \n",
    "        slowMa = ta.EMA(am15.close, self.slowWindow)\n",
    "        self.slowMa0 = slowMa[-1]\n",
    "        self.slowMa1 = slowMa[-2]\n",
    "\n",
    "        # 判断买卖\n",
    "        crossOver = self.fastMa0>self.fastMa1 and self.slowMa0>self.slowMa1     # 均线上涨\n",
    "        crossBelow = self.fastMa0<self.fastMa1 and self.slowMa0<self.slowMa1    # 均线下跌\n",
    "        \n",
    "        # 金叉和死叉的条件是互斥\n",
    "        if crossOver and self.maTrend==1:\n",
    "            # 如果金叉时手头没有持仓，则直接做多\n",
    "            if (self.posDict[self.Longpos]==0) and (self.posDict[self.Shortpos]==0):\n",
    "                self.buy(symbol,bar.close*1.1, 1)\n",
    "            # 如果有空头持仓，则先平空，再做多\n",
    "            elif self.posDict[self.Shortpos] >0:\n",
    "                self.cancelAll()\n",
    "                self.cover(symbol,bar.close*1.1, 1)\n",
    "                self.buy(symbol,bar.close*1.1, 1)\n",
    "\n",
    "        # 死叉和金叉相反\n",
    "        elif crossBelow and self.maTrend==-1:\n",
    "            if (self.posDict[self.Longpos]==0) and (self.posDict[self.Shortpos]==0):\n",
    "                self.short(symbol,bar.close*0.9, 1)\n",
    "            elif self.posDict[self.Longpos] >0:\n",
    "                self.cancelAll()\n",
    "                self.sell(symbol,bar.close*0.9, 1)\n",
    "                self.short(symbol,bar.close*0.9, 1)\n",
    "\n",
    "\n",
    "        # 发出状态更新事件\n",
    "        self.putEvent()\n",
    "    \n",
    "    #----------------------------------------------------------------------\n",
    "    def onOrder(self, order):\n",
    "        \"\"\"收到委托变化推送（必须由用户继承实现）\"\"\"\n",
    "        # 对于无需做细粒度委托控制的策略，可以忽略onOrder\n",
    "        pass\n",
    "    \n",
    "    #----------------------------------------------------------------------\n",
    "    def onTrade(self, trade):\n",
    "        \"\"\"收到成交推送（必须由用户继承实现）\"\"\"\n",
    "        # 对于无需做细粒度委托控制的策略，可以忽略onOrder\n",
    "#         self.transactionPrice = trade.high\n",
    "#         print('high:',self.intraTradeHighDict)\n",
    "#         print('low:',self.intraTradeLowDict)\n",
    "        pass\n",
    "    \n",
    "    #----------------------------------------------------------------------\n",
    "    def onStopOrder(self, so):\n",
    "        \"\"\"停止单推送\"\"\"\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-07-10 21:27:38.190070\t开始回测\n",
      "2018-07-10 21:27:38.190070\t策略初始化\n",
      "2018-07-10 21:27:38.190070\t载入历史数据。数据范围:[20171231,20180101)\n",
      "2018-07-10 21:27:38.218042\t策略初始化完成\n",
      "2018-07-10 21:27:38.218042\t策略启动完成\n",
      "2018-07-10 21:27:38.218042\t开始回放回测数据,回测范围:[20180101,20180701)\n",
      "2018-07-10 21:27:38.219042\t载入历史数据。数据范围:[20180101,20180701)\n",
      "2018-07-10 21:28:00.107686\t载入完成，数据量：259789\n",
      "2018-07-10 21:28:00.107686\t当前回放数据:[20180101,20180701)\n",
      "2018-07-10 21:28:03.607112\t数据回放结束ss: 100%    \n"
     ]
    }
   ],
   "source": [
    "if __name__==\"__main__\":\n",
    "    from vnpy.trader.app.ctaStrategy.ctaBacktesting import BacktestingEngine, OptimizationSetting, MINUTE_DB_NAME\n",
    "    # 创建回测引擎对象\n",
    "    engine = BacktestingEngine()\n",
    "    # 设置回测使用的数据\n",
    "    engine.setBacktestingMode(engine.BAR_MODE)    # 设置引擎的回测模式为K线\n",
    "    engine.setDatabase(MINUTE_DB_NAME)  # 设置使用的历史数据库\n",
    "    engine.setStartDate('20180101',initDays=1)               # 设置回测用的数据起始日期\n",
    "    engine.setEndDate('20180630')\n",
    "    # 配置回测引擎参数\n",
    "    engine.setSlippage(0.2)     # 设置滑点为股指1跳\n",
    "    engine.setRate(1/1000)   # 设置手续费万0.3\n",
    "    engine.setSize(1)         # 设置股指合约大小\n",
    "    # engine.setPriceTick(0.0001)    # 设置股指最小价格变动\n",
    "    engine.setCapital(1000000)  # 设置回测本金\n",
    "\n",
    "    # # 在引擎中创建策略对象\n",
    "    d = {'symbolList':['tBTCUSD:bitfinex']}          # 策略参数配置\n",
    "    engine.initStrategy(TrailingMaATRStrategy, d)    # 创建策略对象\n",
    "    engine.runBacktesting()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-07-10 21:28:03.615103\t计算按日统计结果\n",
      "2018-07-10 21:28:03.638080\t------------------------------\n",
      "2018-07-10 21:28:03.638080\t首个交易日：\t2018-01-01\n",
      "2018-07-10 21:28:03.638080\t最后交易日：\t2018-06-30\n",
      "2018-07-10 21:28:03.638080\t总交易日：\t181\n",
      "2018-07-10 21:28:03.638080\t盈利交易日\t93\n",
      "2018-07-10 21:28:03.638080\t亏损交易日：\t84\n",
      "2018-07-10 21:28:03.638080\t起始资金：\t1000000\n",
      "2018-07-10 21:28:03.639080\t结束资金：\t1,006,479.46\n",
      "2018-07-10 21:28:03.639080\t总收益率：\t0.65%\n",
      "2018-07-10 21:28:03.639080\t年化收益：\t0.86%\n",
      "2018-07-10 21:28:03.639080\t总盈亏：\t6,479.46\n",
      "2018-07-10 21:28:03.639080\t最大回撤: \t-4,682.86\n",
      "2018-07-10 21:28:03.639080\t百分比最大回撤: -0.47%\n",
      "2018-07-10 21:28:03.639080\t总手续费：\t2,000.66\n",
      "2018-07-10 21:28:03.639080\t总滑点：\t42.0\n",
      "2018-07-10 21:28:03.639080\t总成交金额：\t2,000,655.23\n",
      "2018-07-10 21:28:03.639080\t总成交笔数：\t210\n",
      "2018-07-10 21:28:03.640079\t日均盈亏：\t35.8\n",
      "2018-07-10 21:28:03.640079\t日均手续费：\t11.05\n",
      "2018-07-10 21:28:03.640079\t日均滑点：\t0.23\n",
      "2018-07-10 21:28:03.640079\t日均成交金额：\t11,053.34\n",
      "2018-07-10 21:28:03.640079\t日均成交笔数：\t1.16\n",
      "2018-07-10 21:28:03.640079\t日均收益率：\t0.0%\n",
      "2018-07-10 21:28:03.640079\t收益标准差：\t0.05%\n",
      "2018-07-10 21:28:03.640079\tSharpe Ratio：\t1.2\n"
     ]
    },
    {
     "data": {
      "image/png": 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LcxNe+W0IYWMNWdk1dY0seT+S8FM5HIrNRa1WyZ69osvIyKAQoses2niS198/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18oDJ7baxjsVlhBn/9/l5f5tqyibgWGUSR6BtOaJCoxwa7K074a81NTnEFvYwcAQGPGRafjTKg9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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae530f0b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-07-10 21:28:05.411269\t计算回测结果\n",
      "2018-07-10 21:28:05.420261\t------------------------------\n",
      "2018-07-10 21:28:05.420261\t第一笔交易：\t2018-01-04 16:31:00\n",
      "2018-07-10 21:28:05.420261\t最后一笔交易：\t2018-06-30 06:32:00\n",
      "2018-07-10 21:28:05.420261\t总交易次数：\t105\n",
      "2018-07-10 21:28:05.420261\t总盈亏：\t6,479.46\n",
      "2018-07-10 21:28:05.420261\t最大回撤: \t-4,297.41\n",
      "2018-07-10 21:28:05.420261\t平均每笔盈利：\t61.71\n",
      "2018-07-10 21:28:05.420261\t平均每笔滑点：\t0.4\n",
      "2018-07-10 21:28:05.420261\t平均每笔佣金：\t19.05\n",
      "2018-07-10 21:28:05.420261\t胜率\t\t42.86%\n",
      "2018-07-10 21:28:05.420261\t盈利交易平均值\t607.05\n",
      "2018-07-10 21:28:05.420261\t亏损交易平均值\t-347.3\n",
      "2018-07-10 21:28:05.420261\t盈亏比：\t1.75\n"
     ]
    },
    {
     "data": {
      "image/png": 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wLo9H6tFDevVVyce/AQAQrLxKiL/44gu9/vrrOnz4sDwej3Jzc3XkyBF98803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\n4oknsLOzU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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae533ffc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "    # 显示逐日回测结果\n",
    "    engine.showDailyResult()\n",
    "    # 显示逐笔回测结果\n",
    "    engine.showBacktestingResult()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
